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Introduction to Artificial Intelligence智慧树知到课后章节答案2023年下哈尔滨工程大学哈尔滨工程大学第一章测试1.All life has intelligence The following statements about intelligence arewrong（）A:All life has intelligence B:Bacteria do not have intelligence C:At present,human intelligence is the highest level of nature D:From the perspective of life, intelligence is the basic ability of life to adapt to the natural world答案:Bacteria do not have intelligence2.Which of the following techniques is unsupervised learning in artificialintelligence?（）A:Neural network B:Support vector machine C:Decision tree D:Clustering答案:Clustering3.To which period can the history of the development of artificial intelligencebe traced back?（）A:1970s B:Late 19th century C:Early 21st century D:1950s答案:Late 19th century4.Which of the following fields does not belong to the scope of artificialintelligence application?（）A:Aviation B:Medical C:Agriculture D:Finance答案:Aviation5.The first artificial neuron model in human history was the MP model,proposed by Hebb.（）A:对 B:错答案:错6.Big data will bring considerable value in government public services, medicalservices, retail, manufacturing, and personal location services. （）A:错 B:对答案:对第二章测试1.Which of the following options is not human reason:（）A:Value rationality B:Intellectual rationality C:Methodological rationalityD:Cognitive rationality答案:Intellectual rationality2.When did life begin? （）A:Between 10 billion and 4.5 billion years B:Between 13.8 billion years and10 billion years C:Between 4.5 billion and 3.5 billion years D:Before 13.8billion years答案:Between 4.5 billion and 3.5 billion years3.Which of the following statements is true regarding the philosophicalthinking about artificial intelligence?（）A:Philosophical thinking has hindered the progress of artificial intelligence.B:Philosophical thinking has contributed to the development of artificialintelligence. C:Philosophical thinking is only concerned with the ethicalimplications of artificial intelligence. D:Philosophical thinking has no impact on the development of artificial intelligence.答案:Philosophical thinking has contributed to the development ofartificial intelligence.4.What is the rational nature of artificial intelligence?（）A:The ability to communicate effectively with humans. B:The ability to feel emotions and express creativity. C:The ability to reason and make logicaldeductions. D:The ability to learn from experience and adapt to newsituations.答案:The ability to reason and make logical deductions.5.Which of the following statements is true regarding the rational nature ofartificial intelligence?（）A:The rational nature of artificial intelligence includes emotional intelligence.B:The rational nature of artificial intelligence is limited to logical reasoning.C:The rational nature of artificial intelligence is not important for itsdevelopment. D:The rational nature of artificial intelligence is only concerned with mathematical calculations.答案:The rational nature of artificial intelligence is limited to logicalreasoning.6.Connectionism believes that the basic element of human thinking is symbol,not neuron; Human's cognitive process is a self-organization process ofsymbol operation rather than weight. （）A:对 B:错答案:错第三章测试1.The brain of all organisms can be divided into three primitive parts:forebrain, midbrain and hindbrain. Specifically, the human brain is composed of brainstem, cerebellum and brain (forebrain). （）A:错 B:对答案:对2.The neural connections in the brain are chaotic. （）A:对 B:错答案:错3.The following statement about the left and right half of the brain and itsfunction is wrong （）.A:When dictating questions, the left brain is responsible for logical thinking,and the right brain is responsible for language description. B:The left brain is like a scientist, good at abstract thinking and complex calculation, but lacking rich emotion. C:The right brain is like an artist, creative in music, art andother artistic activities, and rich in emotion D:The left and right hemispheres of the brain have the same shape, but their functions are quite different. They are generally called the left brain and the right brain respectively.答案:When dictating questions, the left brain is responsible for logicalthinking, and the right brain is responsible for language description.4.What is the basic unit of the nervous system?（）A:Neuron B:Gene C:Atom D:Molecule答案:Neuron5.What is the role of the prefrontal cortex in cognitive functions?（）A:It is responsible for sensory processing. B:It is involved in emotionalprocessing. C:It is responsible for higher-level cognitive functions. D:It isinvolved in motor control.答案:It is responsible for higher-level cognitive functions.6.What is the definition of intelligence?（）A:The ability to communicate effectively. B:The ability to perform physicaltasks. C:The ability to acquire and apply knowledge and skills. D:The abilityto regulate emotions.答案:The ability to acquire and apply knowledge and skills.第四章测试1.The forward propagation neural network is based on the mathematicalmodel of neurons and is composed of neurons connected together by specific connection methods. Different artificial neural networks generally havedifferent structures, but the basis is still the mathematical model of neurons.（）A:对 B:错答案:对2.In the perceptron, the weights are adjusted by learning so that the networkcan get the desired output for any input. （）A:对 B:错答案:对3.Convolution neural network is a feedforward neural network, which hasmany advantages and has excellent performance for large image processing.Among the following options, the advantage of convolution neural network is（）.A:Implicit learning avoids explicit feature extraction B:Weight sharingC:Translation invariance D:Strong robustness答案:Implicit learning avoids explicit feature extraction;Weightsharing;Strong robustness4.In a feedforward neural network, information travels in which direction?（）A:Forward B:Both A and B C:None of the above D:Backward答案:Forward5.What is the main feature of a convolutional neural network?（）A:They are used for speech recognition. B:They are used for natural languageprocessing. C:They are used for reinforcement learning. D:They are used forimage recognition.答案:They are used for image recognition.6.Which of the following is a characteristic of deep neural networks?（）A:They require less training data than shallow neural networks. B:They havefewer hidden layers than shallow neural networks. C:They have loweraccuracy than shallow neural networks. D:They are more computationallyexpensive than shallow neural networks.答案:They are more computationally expensive than shallow neuralnetworks.第五章测试1.Machine learning refers to how the computer simulates or realizes humanlearning behavior to obtain new knowledge or skills, and reorganizes the existing knowledge structure to continuously improve its own performance.（）A:对 B:错答案:对2.The best decision sequence of Markov decision process is solved by Bellmanequation, and the value of each state is determined not only by the current state but also by the later state.（）A:对 B:错答案:对3.Alex Net's contributions to this work include: （）.A:Use GPUNVIDIAGTX580 to reduce the training time B:Use the modified linear unit (Re LU) as the nonlinear activation function C:Cover the larger pool to avoid the average effect of average pool D:Use the Dropouttechnology to selectively ignore the single neuron during training to avoid over-fitting the model答案:Use GPUNVIDIAGTX580 to reduce the training time;Use themodified linear unit (Re LU) as the nonlinear activation function;Cover the larger pool to avoid the average effect of average pool;Use theDropout technology to selectively ignore the single neuron duringtraining to avoid over-fitting the model4.In supervised learning, what is the role of the labeled data?（）A:To evaluate the model B:To train the model C:None of the above D:To test the model答案:To train the model5.In reinforcement learning, what is the goal of the agent?（）A:To identify patterns in input data B:To minimize the error between thepredicted and actual output C:To maximize the reward obtained from theenvironment D:To classify input data into different categories答案:To maximize the reward obtained from the environment6.Which of the following is a characteristic of transfer learning?（）A:It can only be used for supervised learning tasks B:It requires a largeamount of labeled data C:It involves transferring knowledge from onedomain to another D:It is only applicable to small-scale problems答案:It involves transferring knowledge from one domain to another第六章测试1.Image segmentation is the technology and process of dividing an image intoseveral specific regions with unique properties and proposing objects ofinterest. In the following statement about image segmentation algorithm, the error is （）.A:Region growth method is to complete the segmentation by calculating the mean vector of the offset. B:Watershed algorithm, MeanShift segmentation,region growth and Ostu threshold segmentation can complete imagesegmentation. C:Watershed algorithm is often used to segment the objectsconnected in the image. D:Otsu threshold segmentation, also known as themaximum between-class difference method, realizes the automatic selection of global threshold T by counting the histogram characteristics of the entire image答案:Region growth method is to complete the segmentation bycalculating the mean vector of the offset.2.Camera calibration is a key step when using machine vision to measureobjects. Its calibration accuracy will directly affect the measurementaccuracy. Among them, camera calibration generally involves the mutualconversion of object point coordinates in several coordinate systems. So,what coordinate systems do you mean by "several coordinate systems" here?（）A:Image coordinate system B:Image plane coordinate system C:Cameracoordinate system D:World coordinate system答案:Image coordinate system;Image plane coordinate system;Camera coordinate system;World coordinate systemmonly used digital image filtering methods:（）.A:bilateral filtering B:median filter C:mean filtering D:Gaussian filter答案:bilateral filtering;median filter;mean filtering;Gaussian filter4.Application areas of digital image processing include:（）A:Industrial inspection B:Biomedical Science C:Scenario simulation D:remote sensing答案:Industrial inspection;Biomedical Science5.Image segmentation is the technology and process of dividing an image intoseveral specific regions with unique properties and proposing objects ofinterest. In the following statement about image segmentation algorithm, the error is ( ).A:Otsu threshold segmentation, also known as the maximum between-class difference method, realizes the automatic selection of global threshold T by counting the histogram characteristics of the entire imageB: Watershed algorithm is often used to segment the objects connected in the image. C:Region growth method is to complete the segmentation bycalculating the mean vector of the offset. D:Watershed algorithm, MeanShift segmentation, region growth and Ostu threshold segmentation can complete image segmentation.答案:Region growth method is to complete the segmentation bycalculating the mean vector of the offset.第七章测试1.Blind search can be applied to many different search problems, but it has notbeen widely used due to its low efficiency.（）A:错 B:对答案:对2.Which of the following search methods uses a FIFO queue （）.A:width-first search B:random search C:depth-first search D:generation-test method答案:width-first search3.What causes the complexity of the semantic network （）.A:There is no recognized formal representation system B:The quantifiernetwork is inadequate C:The means of knowledge representation are diverse D:The relationship between nodes can be linear, nonlinear, or even recursive 答案:The means of knowledge representation are diverse;Therelationship between nodes can be linear, nonlinear, or even recursive4.In the knowledge graph taking Leonardo da Vinci as an example, the entity ofthe character represents a node, and the relationship between the artist and the character represents an edge. Search is the process of finding the actionsequence of an intelligent system.（）A:对 B:错答案:对5.Which of the following statements about common methods of path search iswrong（）A:When using the artificial potential field method, when there are someobstacles in any distance around the target point, it is easy to cause the path to be unreachable B:The A* algorithm occupies too much memory during the search, the search efficiency is reduced, and the optimal result cannot beguaranteed C:The artificial potential field method can quickly search for acollision-free path with strong flexibility D:A* algorithm can solve theshortest path of state space search答案:When using the artificial potential field method, when there aresome obstacles in any distance around the target point, it is easy tocause the path to be unreachable第八章测试1.The language, spoken language, written language, sign language and Pythonlanguage of human communication are all natural languages.（）A:对 B:错答案:错2.The following statement about machine translation is wrong （）.A:The analysis stage of machine translation is mainly lexical analysis andpragmatic analysis B:The essence of machine translation is the discovery and application of bilingual translation laws. C:The four stages of machinetranslation are retrieval, analysis, conversion and generation. D:At present,natural language machine translation generally takes sentences as thetranslation unit.答案:The analysis stage of machine translation is mainly lexical analysis and pragmatic analysis3.Which of the following fields does machine translation belong to? （）A:Expert system B:Machine learning C:Human sensory simulation D:Natural language system答案:Natural language system4.The following statements about language are wrong: （）。
q-learning公式解释在强化学习领域中，Q-learning是一种用来解决延迟回报问题的经典算法。
它是一种基于值函数的算法，通常用来解决马尔科夫决策过程（MDP）的问题。
Q-learning的核心思想是通过不断地更新一个状态动作值函数（Q值函数），以达到最优策略的目标。
本文将从Q-learning的基本原理、算法公式和应用场景等方面对Q-learning进行详细解释，以帮助读者更好地理解Q-learning的概念和运行原理。
1.基本原理Q-learning的基本原理可以通过马尔科夫决策过程（MDP）来理解。
MDP是一种用来描述决策过程的数学模型，它包括一个状态空间和一个动作空间，以及一个奖励函数和状态转移概率。
在MDP中，智能体通过选择动作来改变状态，并且会收到相应的奖励或惩罚。
其目标是找到一个最优的策略，以最大化长期回报。
Q-learning是一种基于值迭代的强化学习算法，它的目标是学习一个最优的价值函数。
这个价值函数可以用来评估在任何状态下采取任何动作的好坏程度，以帮助智能体做出最优的决策。
Q值函数可以通过下面的公式来定义：\[Q(s,a) = (1-\alpha) Q(s,a) + \alpha (r + \gamma\max_{a'} Q(s',a'))\]其中，\(Q(s,a)\)表示在状态\(s\)下采取动作\(a\)的价值，\(\alpha\)表示学习率，\(r\)表示当前状态下的即时奖励，\(\gamma\)表示折扣因子，\(s'\)表示下一个状态，\(a'\)表示在下一个状态下的动作。
Q-learning的核心思想是通过不断地更新Q值函数，使得智能体在每一步都能做出最优的动作选择。
当Q值函数收敛时，智能体可以根据Q值函数选择最优动作，从而达到最优策略。
2.算法公式Q-learning算法的更新公式可以用下面的伪代码来表示：```初始化Q值函数Q(s,a)为任意值重复执行以下步骤：1.选择一个动作a，用来改变当前状态s2.执行动作a，观察下一个状态s'和即时奖励r3.更新Q值函数：Q(s,a) = (1-\alpha) Q(s,a) + \alpha (r +\gamma \max_{a'} Q(s',a'))4.将状态s更新为s'直到收敛```在伪代码中，\(\alpha\)表示学习率，\(\gamma\)表示折扣因子。
机器学习与人工智能领域中常用的英语词汇1.General Concepts (基础概念)•Artificial Intelligence (AI) - 人工智能1)Artificial Intelligence (AI) - 人工智能2)Machine Learning (ML) - 机器学习3)Deep Learning (DL) - 深度学习4)Neural Network - 神经网络5)Natural Language Processing (NLP) - 自然语言处理6)Computer Vision - 计算机视觉7)Robotics - 机器人技术8)Speech Recognition - 语音识别9)Expert Systems - 专家系统10)Knowledge Representation - 知识表示11)Pattern Recognition - 模式识别12)Cognitive Computing - 认知计算13)Autonomous Systems - 自主系统14)Human-Machine Interaction - 人机交互15)Intelligent Agents - 智能代理16)Machine Translation - 机器翻译17)Swarm Intelligence - 群体智能18)Genetic Algorithms - 遗传算法19)Fuzzy Logic - 模糊逻辑20)Reinforcement Learning - 强化学习•Machine Learning (ML) - 机器学习1)Machine Learning (ML) - 机器学习2)Artificial Neural Network - 人工神经网络3)Deep Learning - 深度学习4)Supervised Learning - 有监督学习5)Unsupervised Learning - 无监督学习6)Reinforcement Learning - 强化学习7)Semi-Supervised Learning - 半监督学习8)Training Data - 训练数据9)Test Data - 测试数据10)Validation Data - 验证数据11)Feature - 特征12)Label - 标签13)Model - 模型14)Algorithm - 算法15)Regression - 回归16)Classification - 分类17)Clustering - 聚类18)Dimensionality Reduction - 降维19)Overfitting - 过拟合20)Underfitting - 欠拟合•Deep Learning (DL) - 深度学习1)Deep Learning - 深度学习2)Neural Network - 神经网络3)Artificial Neural Network (ANN) - 人工神经网络4)Convolutional Neural Network (CNN) - 卷积神经网络5)Recurrent Neural Network (RNN) - 循环神经网络6)Long Short-Term Memory (LSTM) - 长短期记忆网络7)Gated Recurrent Unit (GRU) - 门控循环单元8)Autoencoder - 自编码器9)Generative Adversarial Network (GAN) - 生成对抗网络10)Transfer Learning - 迁移学习11)Pre-trained Model - 预训练模型12)Fine-tuning - 微调13)Feature Extraction - 特征提取14)Activation Function - 激活函数15)Loss Function - 损失函数16)Gradient Descent - 梯度下降17)Backpropagation - 反向传播18)Epoch - 训练周期19)Batch Size - 批量大小20)Dropout - 丢弃法•Neural Network - 神经网络1)Neural Network - 神经网络2)Artificial Neural Network (ANN) - 人工神经网络3)Deep Neural Network (DNN) - 深度神经网络4)Convolutional Neural Network (CNN) - 卷积神经网络5)Recurrent Neural Network (RNN) - 循环神经网络6)Long Short-Term Memory (LSTM) - 长短期记忆网络7)Gated Recurrent Unit (GRU) - 门控循环单元8)Feedforward Neural Network - 前馈神经网络9)Multi-layer Perceptron (MLP) - 多层感知器10)Radial Basis Function Network (RBFN) - 径向基函数网络11)Hopfield Network - 霍普菲尔德网络12)Boltzmann Machine - 玻尔兹曼机13)Autoencoder - 自编码器14)Spiking Neural Network (SNN) - 脉冲神经网络15)Self-organizing Map (SOM) - 自组织映射16)Restricted Boltzmann Machine (RBM) - 受限玻尔兹曼机17)Hebbian Learning - 海比安学习18)Competitive Learning - 竞争学习19)Neuroevolutionary - 神经进化20)Neuron - 神经元•Algorithm - 算法1)Algorithm - 算法2)Supervised Learning Algorithm - 有监督学习算法3)Unsupervised Learning Algorithm - 无监督学习算法4)Reinforcement Learning Algorithm - 强化学习算法5)Classification Algorithm - 分类算法6)Regression Algorithm - 回归算法7)Clustering Algorithm - 聚类算法8)Dimensionality Reduction Algorithm - 降维算法9)Decision Tree Algorithm - 决策树算法10)Random Forest Algorithm - 随机森林算法11)Support Vector Machine (SVM) Algorithm - 支持向量机算法12)K-Nearest Neighbors (KNN) Algorithm - K近邻算法13)Naive Bayes Algorithm - 朴素贝叶斯算法14)Gradient Descent Algorithm - 梯度下降算法15)Genetic Algorithm - 遗传算法16)Neural Network Algorithm - 神经网络算法17)Deep Learning Algorithm - 深度学习算法18)Ensemble Learning Algorithm - 集成学习算法19)Reinforcement Learning Algorithm - 强化学习算法20)Metaheuristic Algorithm - 元启发式算法•Model - 模型1)Model - 模型2)Machine Learning Model - 机器学习模型3)Artificial Intelligence Model - 人工智能模型4)Predictive Model - 预测模型5)Classification Model - 分类模型6)Regression Model - 回归模型7)Generative Model - 生成模型8)Discriminative Model - 判别模型9)Probabilistic Model - 概率模型10)Statistical Model - 统计模型11)Neural Network Model - 神经网络模型12)Deep Learning Model - 深度学习模型13)Ensemble Model - 集成模型14)Reinforcement Learning Model - 强化学习模型15)Support Vector Machine (SVM) Model - 支持向量机模型16)Decision Tree Model - 决策树模型17)Random Forest Model - 随机森林模型18)Naive Bayes Model - 朴素贝叶斯模型19)Autoencoder Model - 自编码器模型20)Convolutional Neural Network (CNN) Model - 卷积神经网络模型•Dataset - 数据集1)Dataset - 数据集2)Training Dataset - 训练数据集3)Test Dataset - 测试数据集4)Validation Dataset - 验证数据集5)Balanced Dataset - 平衡数据集6)Imbalanced Dataset - 不平衡数据集7)Synthetic Dataset - 合成数据集8)Benchmark Dataset - 基准数据集9)Open Dataset - 开放数据集10)Labeled Dataset - 标记数据集11)Unlabeled Dataset - 未标记数据集12)Semi-Supervised Dataset - 半监督数据集13)Multiclass Dataset - 多分类数据集14)Feature Set - 特征集15)Data Augmentation - 数据增强16)Data Preprocessing - 数据预处理17)Missing Data - 缺失数据18)Outlier Detection - 异常值检测19)Data Imputation - 数据插补20)Metadata - 元数据•Training - 训练1)Training - 训练2)Training Data - 训练数据3)Training Phase - 训练阶段4)Training Set - 训练集5)Training Examples - 训练样本6)Training Instance - 训练实例7)Training Algorithm - 训练算法8)Training Model - 训练模型9)Training Process - 训练过程10)Training Loss - 训练损失11)Training Epoch - 训练周期12)Training Batch - 训练批次13)Online Training - 在线训练14)Offline Training - 离线训练15)Continuous Training - 连续训练16)Transfer Learning - 迁移学习17)Fine-Tuning - 微调18)Curriculum Learning - 课程学习19)Self-Supervised Learning - 自监督学习20)Active Learning - 主动学习•Testing - 测试1)Testing - 测试2)Test Data - 测试数据3)Test Set - 测试集4)Test Examples - 测试样本5)Test Instance - 测试实例6)Test Phase - 测试阶段7)Test Accuracy - 测试准确率8)Test Loss - 测试损失9)Test Error - 测试错误10)Test Metrics - 测试指标11)Test Suite - 测试套件12)Test Case - 测试用例13)Test Coverage - 测试覆盖率14)Cross-Validation - 交叉验证15)Holdout Validation - 留出验证16)K-Fold Cross-Validation - K折交叉验证17)Stratified Cross-Validation - 分层交叉验证18)Test Driven Development (TDD) - 测试驱动开发19)A/B Testing - A/B 测试20)Model Evaluation - 模型评估•Validation - 验证1)Validation - 验证2)Validation Data - 验证数据3)Validation Set - 验证集4)Validation Examples - 验证样本5)Validation Instance - 验证实例6)Validation Phase - 验证阶段7)Validation Accuracy - 验证准确率8)Validation Loss - 验证损失9)Validation Error - 验证错误10)Validation Metrics - 验证指标11)Cross-Validation - 交叉验证12)Holdout Validation - 留出验证13)K-Fold Cross-Validation - K折交叉验证14)Stratified Cross-Validation - 分层交叉验证15)Leave-One-Out Cross-Validation - 留一法交叉验证16)Validation Curve - 验证曲线17)Hyperparameter Validation - 超参数验证18)Model Validation - 模型验证19)Early Stopping - 提前停止20)Validation Strategy - 验证策略•Supervised Learning - 有监督学习1)Supervised Learning - 有监督学习2)Label - 标签3)Feature - 特征4)Target - 目标5)Training Labels - 训练标签6)Training Features - 训练特征7)Training Targets - 训练目标8)Training Examples - 训练样本9)Training Instance - 训练实例10)Regression - 回归11)Classification - 分类12)Predictor - 预测器13)Regression Model - 回归模型14)Classifier - 分类器15)Decision Tree - 决策树16)Support Vector Machine (SVM) - 支持向量机17)Neural Network - 神经网络18)Feature Engineering - 特征工程19)Model Evaluation - 模型评估20)Overfitting - 过拟合21)Underfitting - 欠拟合22)Bias-Variance Tradeoff - 偏差-方差权衡•Unsupervised Learning - 无监督学习1)Unsupervised Learning - 无监督学习2)Clustering - 聚类3)Dimensionality Reduction - 降维4)Anomaly Detection - 异常检测5)Association Rule Learning - 关联规则学习6)Feature Extraction - 特征提取7)Feature Selection - 特征选择8)K-Means - K均值9)Hierarchical Clustering - 层次聚类10)Density-Based Clustering - 基于密度的聚类11)Principal Component Analysis (PCA) - 主成分分析12)Independent Component Analysis (ICA) - 独立成分分析13)T-distributed Stochastic Neighbor Embedding (t-SNE) - t分布随机邻居嵌入14)Gaussian Mixture Model (GMM) - 高斯混合模型15)Self-Organizing Maps (SOM) - 自组织映射16)Autoencoder - 自动编码器17)Latent Variable - 潜变量18)Data Preprocessing - 数据预处理19)Outlier Detection - 异常值检测20)Clustering Algorithm - 聚类算法•Reinforcement Learning - 强化学习1)Reinforcement Learning - 强化学习2)Agent - 代理3)Environment - 环境4)State - 状态5)Action - 动作6)Reward - 奖励7)Policy - 策略8)Value Function - 值函数9)Q-Learning - Q学习10)Deep Q-Network (DQN) - 深度Q网络11)Policy Gradient - 策略梯度12)Actor-Critic - 演员-评论家13)Exploration - 探索14)Exploitation - 开发15)Temporal Difference (TD) - 时间差分16)Markov Decision Process (MDP) - 马尔可夫决策过程17)State-Action-Reward-State-Action (SARSA) - 状态-动作-奖励-状态-动作18)Policy Iteration - 策略迭代19)Value Iteration - 值迭代20)Monte Carlo Methods - 蒙特卡洛方法•Semi-Supervised Learning - 半监督学习1)Semi-Supervised Learning - 半监督学习2)Labeled Data - 有标签数据3)Unlabeled Data - 无标签数据4)Label Propagation - 标签传播5)Self-Training - 自训练6)Co-Training - 协同训练7)Transudative Learning - 传导学习8)Inductive Learning - 归纳学习9)Manifold Regularization - 流形正则化10)Graph-based Methods - 基于图的方法11)Cluster Assumption - 聚类假设12)Low-Density Separation - 低密度分离13)Semi-Supervised Support Vector Machines (S3VM) - 半监督支持向量机14)Expectation-Maximization (EM) - 期望最大化15)Co-EM - 协同期望最大化16)Entropy-Regularized EM - 熵正则化EM17)Mean Teacher - 平均教师18)Virtual Adversarial Training - 虚拟对抗训练19)Tri-training - 三重训练20)Mix Match - 混合匹配•Feature - 特征1)Feature - 特征2)Feature Engineering - 特征工程3)Feature Extraction - 特征提取4)Feature Selection - 特征选择5)Input Features - 输入特征6)Output Features - 输出特征7)Feature Vector - 特征向量8)Feature Space - 特征空间9)Feature Representation - 特征表示10)Feature Transformation - 特征转换11)Feature Importance - 特征重要性12)Feature Scaling - 特征缩放13)Feature Normalization - 特征归一化14)Feature Encoding - 特征编码15)Feature Fusion - 特征融合16)Feature Dimensionality Reduction - 特征维度减少17)Continuous Feature - 连续特征18)Categorical Feature - 分类特征19)Nominal Feature - 名义特征20)Ordinal Feature - 有序特征•Label - 标签1)Label - 标签2)Labeling - 标注3)Ground Truth - 地面真值4)Class Label - 类别标签5)Target Variable - 目标变量6)Labeling Scheme - 标注方案7)Multi-class Labeling - 多类别标注8)Binary Labeling - 二分类标注9)Label Noise - 标签噪声10)Labeling Error - 标注错误11)Label Propagation - 标签传播12)Unlabeled Data - 无标签数据13)Labeled Data - 有标签数据14)Semi-supervised Learning - 半监督学习15)Active Learning - 主动学习16)Weakly Supervised Learning - 弱监督学习17)Noisy Label Learning - 噪声标签学习18)Self-training - 自训练19)Crowdsourcing Labeling - 众包标注20)Label Smoothing - 标签平滑化•Prediction - 预测1)Prediction - 预测2)Forecasting - 预测3)Regression - 回归4)Classification - 分类5)Time Series Prediction - 时间序列预测6)Forecast Accuracy - 预测准确性7)Predictive Modeling - 预测建模8)Predictive Analytics - 预测分析9)Forecasting Method - 预测方法10)Predictive Performance - 预测性能11)Predictive Power - 预测能力12)Prediction Error - 预测误差13)Prediction Interval - 预测区间14)Prediction Model - 预测模型15)Predictive Uncertainty - 预测不确定性16)Forecast Horizon - 预测时间跨度17)Predictive Maintenance - 预测性维护18)Predictive Policing - 预测式警务19)Predictive Healthcare - 预测性医疗20)Predictive Maintenance - 预测性维护•Classification - 分类1)Classification - 分类2)Classifier - 分类器3)Class - 类别4)Classify - 对数据进行分类5)Class Label - 类别标签6)Binary Classification - 二元分类7)Multiclass Classification - 多类分类8)Class Probability - 类别概率9)Decision Boundary - 决策边界10)Decision Tree - 决策树11)Support Vector Machine (SVM) - 支持向量机12)K-Nearest Neighbors (KNN) - K最近邻算法13)Naive Bayes - 朴素贝叶斯14)Logistic Regression - 逻辑回归15)Random Forest - 随机森林16)Neural Network - 神经网络17)SoftMax Function - SoftMax函数18)One-vs-All (One-vs-Rest) - 一对多(一对剩余)19)Ensemble Learning - 集成学习20)Confusion Matrix - 混淆矩阵•Regression - 回归1)Regression Analysis - 回归分析2)Linear Regression - 线性回归3)Multiple Regression - 多元回归4)Polynomial Regression - 多项式回归5)Logistic Regression - 逻辑回归6)Ridge Regression - 岭回归7)Lasso Regression - Lasso回归8)Elastic Net Regression - 弹性网络回归9)Regression Coefficients - 回归系数10)Residuals - 残差11)Ordinary Least Squares (OLS) - 普通最小二乘法12)Ridge Regression Coefficient - 岭回归系数13)Lasso Regression Coefficient - Lasso回归系数14)Elastic Net Regression Coefficient - 弹性网络回归系数15)Regression Line - 回归线16)Prediction Error - 预测误差17)Regression Model - 回归模型18)Nonlinear Regression - 非线性回归19)Generalized Linear Models (GLM) - 广义线性模型20)Coefficient of Determination (R-squared) - 决定系数21)F-test - F检验22)Homoscedasticity - 同方差性23)Heteroscedasticity - 异方差性24)Autocorrelation - 自相关25)Multicollinearity - 多重共线性26)Outliers - 异常值27)Cross-validation - 交叉验证28)Feature Selection - 特征选择29)Feature Engineering - 特征工程30)Regularization - 正则化2.Neural Networks and Deep Learning (神经网络与深度学习)•Convolutional Neural Network (CNN) - 卷积神经网络1)Convolutional Neural Network (CNN) - 卷积神经网络2)Convolution Layer - 卷积层3)Feature Map - 特征图4)Convolution Operation - 卷积操作5)Stride - 步幅6)Padding - 填充7)Pooling Layer - 池化层8)Max Pooling - 最大池化9)Average Pooling - 平均池化10)Fully Connected Layer - 全连接层11)Activation Function - 激活函数12)Rectified Linear Unit (ReLU) - 线性修正单元13)Dropout - 随机失活14)Batch Normalization - 批量归一化15)Transfer Learning - 迁移学习16)Fine-Tuning - 微调17)Image Classification - 图像分类18)Object Detection - 物体检测19)Semantic Segmentation - 语义分割20)Instance Segmentation - 实例分割21)Generative Adversarial Network (GAN) - 生成对抗网络22)Image Generation - 图像生成23)Style Transfer - 风格迁移24)Convolutional Autoencoder - 卷积自编码器25)Recurrent Neural Network (RNN) - 循环神经网络•Recurrent Neural Network (RNN) - 循环神经网络1)Recurrent Neural Network (RNN) - 循环神经网络2)Long Short-Term Memory (LSTM) - 长短期记忆网络3)Gated Recurrent Unit (GRU) - 门控循环单元4)Sequence Modeling - 序列建模5)Time Series Prediction - 时间序列预测6)Natural Language Processing (NLP) - 自然语言处理7)Text Generation - 文本生成8)Sentiment Analysis - 情感分析9)Named Entity Recognition (NER) - 命名实体识别10)Part-of-Speech Tagging (POS Tagging) - 词性标注11)Sequence-to-Sequence (Seq2Seq) - 序列到序列12)Attention Mechanism - 注意力机制13)Encoder-Decoder Architecture - 编码器-解码器架构14)Bidirectional RNN - 双向循环神经网络15)Teacher Forcing - 强制教师法16)Backpropagation Through Time (BPTT) - 通过时间的反向传播17)Vanishing Gradient Problem - 梯度消失问题18)Exploding Gradient Problem - 梯度爆炸问题19)Language Modeling - 语言建模20)Speech Recognition - 语音识别•Long Short-Term Memory (LSTM) - 长短期记忆网络1)Long Short-Term Memory (LSTM) - 长短期记忆网络2)Cell State - 细胞状态3)Hidden State - 隐藏状态4)Forget Gate - 遗忘门5)Input Gate - 输入门6)Output Gate - 输出门7)Peephole Connections - 窥视孔连接8)Gated Recurrent Unit (GRU) - 门控循环单元9)Vanishing Gradient Problem - 梯度消失问题10)Exploding Gradient Problem - 梯度爆炸问题11)Sequence Modeling - 序列建模12)Time Series Prediction - 时间序列预测13)Natural Language Processing (NLP) - 自然语言处理14)Text Generation - 文本生成15)Sentiment Analysis - 情感分析16)Named Entity Recognition (NER) - 命名实体识别17)Part-of-Speech Tagging (POS Tagging) - 词性标注18)Attention Mechanism - 注意力机制19)Encoder-Decoder Architecture - 编码器-解码器架构20)Bidirectional LSTM - 双向长短期记忆网络•Attention Mechanism - 注意力机制1)Attention Mechanism - 注意力机制2)Self-Attention - 自注意力3)Multi-Head Attention - 多头注意力4)Transformer - 变换器5)Query - 查询6)Key - 键7)Value - 值8)Query-Value Attention - 查询-值注意力9)Dot-Product Attention - 点积注意力10)Scaled Dot-Product Attention - 缩放点积注意力11)Additive Attention - 加性注意力12)Context Vector - 上下文向量13)Attention Score - 注意力分数14)SoftMax Function - SoftMax函数15)Attention Weight - 注意力权重16)Global Attention - 全局注意力17)Local Attention - 局部注意力18)Positional Encoding - 位置编码19)Encoder-Decoder Attention - 编码器-解码器注意力20)Cross-Modal Attention - 跨模态注意力•Generative Adversarial Network (GAN) - 生成对抗网络1)Generative Adversarial Network (GAN) - 生成对抗网络2)Generator - 生成器3)Discriminator - 判别器4)Adversarial Training - 对抗训练5)Minimax Game - 极小极大博弈6)Nash Equilibrium - 纳什均衡7)Mode Collapse - 模式崩溃8)Training Stability - 训练稳定性9)Loss Function - 损失函数10)Discriminative Loss - 判别损失11)Generative Loss - 生成损失12)Wasserstein GAN (WGAN) - Wasserstein GAN（WGAN）13)Deep Convolutional GAN (DCGAN) - 深度卷积生成对抗网络（DCGAN）14)Conditional GAN (c GAN) - 条件生成对抗网络（c GAN）15)Style GAN - 风格生成对抗网络16)Cycle GAN - 循环生成对抗网络17)Progressive Growing GAN (PGGAN) - 渐进式增长生成对抗网络（PGGAN）18)Self-Attention GAN (SAGAN) - 自注意力生成对抗网络（SAGAN）19)Big GAN - 大规模生成对抗网络20)Adversarial Examples - 对抗样本•Encoder-Decoder - 编码器-解码器1)Encoder-Decoder Architecture - 编码器-解码器架构2)Encoder - 编码器3)Decoder - 解码器4)Sequence-to-Sequence Model (Seq2Seq) - 序列到序列模型5)State Vector - 状态向量6)Context Vector - 上下文向量7)Hidden State - 隐藏状态8)Attention Mechanism - 注意力机制9)Teacher Forcing - 强制教师法10)Beam Search - 束搜索11)Recurrent Neural Network (RNN) - 循环神经网络12)Long Short-Term Memory (LSTM) - 长短期记忆网络13)Gated Recurrent Unit (GRU) - 门控循环单元14)Bidirectional Encoder - 双向编码器15)Greedy Decoding - 贪婪解码16)Masking - 遮盖17)Dropout - 随机失活18)Embedding Layer - 嵌入层19)Cross-Entropy Loss - 交叉熵损失20)Tokenization - 令牌化•Transfer Learning - 迁移学习1)Transfer Learning - 迁移学习2)Source Domain - 源领域3)Target Domain - 目标领域4)Fine-Tuning - 微调5)Domain Adaptation - 领域自适应6)Pre-Trained Model - 预训练模型7)Feature Extraction - 特征提取8)Knowledge Transfer - 知识迁移9)Unsupervised Domain Adaptation - 无监督领域自适应10)Semi-Supervised Domain Adaptation - 半监督领域自适应11)Multi-Task Learning - 多任务学习12)Data Augmentation - 数据增强13)Task Transfer - 任务迁移14)Model Agnostic Meta-Learning (MAML) - 与模型无关的元学习（MAML）15)One-Shot Learning - 单样本学习16)Zero-Shot Learning - 零样本学习17)Few-Shot Learning - 少样本学习18)Knowledge Distillation - 知识蒸馏19)Representation Learning - 表征学习20)Adversarial Transfer Learning - 对抗迁移学习•Pre-trained Models - 预训练模型1)Pre-trained Model - 预训练模型2)Transfer Learning - 迁移学习3)Fine-Tuning - 微调4)Knowledge Transfer - 知识迁移5)Domain Adaptation - 领域自适应6)Feature Extraction - 特征提取7)Representation Learning - 表征学习8)Language Model - 语言模型9)Bidirectional Encoder Representations from Transformers (BERT) - 双向编码器结构转换器10)Generative Pre-trained Transformer (GPT) - 生成式预训练转换器11)Transformer-based Models - 基于转换器的模型12)Masked Language Model (MLM) - 掩蔽语言模型13)Cloze Task - 填空任务14)Tokenization - 令牌化15)Word Embeddings - 词嵌入16)Sentence Embeddings - 句子嵌入17)Contextual Embeddings - 上下文嵌入18)Self-Supervised Learning - 自监督学习19)Large-Scale Pre-trained Models - 大规模预训练模型•Loss Function - 损失函数1)Loss Function - 损失函数2)Mean Squared Error (MSE) - 均方误差3)Mean Absolute Error (MAE) - 平均绝对误差4)Cross-Entropy Loss - 交叉熵损失5)Binary Cross-Entropy Loss - 二元交叉熵损失6)Categorical Cross-Entropy Loss - 分类交叉熵损失7)Hinge Loss - 合页损失8)Huber Loss - Huber损失9)Wasserstein Distance - Wasserstein距离10)Triplet Loss - 三元组损失11)Contrastive Loss - 对比损失12)Dice Loss - Dice损失13)Focal Loss - 焦点损失14)GAN Loss - GAN损失15)Adversarial Loss - 对抗损失16)L1 Loss - L1损失17)L2 Loss - L2损失18)Huber Loss - Huber损失19)Quantile Loss - 分位数损失•Activation Function - 激活函数1)Activation Function - 激活函数2)Sigmoid Function - Sigmoid函数3)Hyperbolic Tangent Function (Tanh) - 双曲正切函数4)Rectified Linear Unit (Re LU) - 矩形线性单元5)Parametric Re LU (P Re LU) - 参数化Re LU6)Exponential Linear Unit (ELU) - 指数线性单元7)Swish Function - Swish函数8)Softplus Function - Soft plus函数9)Softmax Function - SoftMax函数10)Hard Tanh Function - 硬双曲正切函数11)Softsign Function - Softsign函数12)GELU (Gaussian Error Linear Unit) - GELU（高斯误差线性单元）13)Mish Function - Mish函数14)CELU (Continuous Exponential Linear Unit) - CELU（连续指数线性单元）15)Bent Identity Function - 弯曲恒等函数16)Gaussian Error Linear Units (GELUs) - 高斯误差线性单元17)Adaptive Piecewise Linear (APL) - 自适应分段线性函数18)Radial Basis Function (RBF) - 径向基函数•Backpropagation - 反向传播1)Backpropagation - 反向传播2)Gradient Descent - 梯度下降3)Partial Derivative - 偏导数4)Chain Rule - 链式法则5)Forward Pass - 前向传播6)Backward Pass - 反向传播7)Computational Graph - 计算图8)Neural Network - 神经网络9)Loss Function - 损失函数10)Gradient Calculation - 梯度计算11)Weight Update - 权重更新12)Activation Function - 激活函数13)Optimizer - 优化器14)Learning Rate - 学习率15)Mini-Batch Gradient Descent - 小批量梯度下降16)Stochastic Gradient Descent (SGD) - 随机梯度下降17)Batch Gradient Descent - 批量梯度下降18)Momentum - 动量19)Adam Optimizer - Adam优化器20)Learning Rate Decay - 学习率衰减•Gradient Descent - 梯度下降1)Gradient Descent - 梯度下降2)Stochastic Gradient Descent (SGD) - 随机梯度下降3)Mini-Batch Gradient Descent - 小批量梯度下降4)Batch Gradient Descent - 批量梯度下降5)Learning Rate - 学习率6)Momentum - 动量7)Adaptive Moment Estimation (Adam) - 自适应矩估计8)RMSprop - 均方根传播9)Learning Rate Schedule - 学习率调度10)Convergence - 收敛11)Divergence - 发散12)Adagrad - 自适应学习速率方法13)Adadelta - 自适应增量学习率方法14)Adamax - 自适应矩估计的扩展版本15)Nadam - Nesterov Accelerated Adaptive Moment Estimation16)Learning Rate Decay - 学习率衰减17)Step Size - 步长18)Conjugate Gradient Descent - 共轭梯度下降19)Line Search - 线搜索20)Newton's Method - 牛顿法•Learning Rate - 学习率1)Learning Rate - 学习率2)Adaptive Learning Rate - 自适应学习率3)Learning Rate Decay - 学习率衰减4)Initial Learning Rate - 初始学习率5)Step Size - 步长6)Momentum - 动量7)Exponential Decay - 指数衰减8)Annealing - 退火9)Cyclical Learning Rate - 循环学习率10)Learning Rate Schedule - 学习率调度11)Warm-up - 预热12)Learning Rate Policy - 学习率策略13)Learning Rate Annealing - 学习率退火14)Cosine Annealing - 余弦退火15)Gradient Clipping - 梯度裁剪16)Adapting Learning Rate - 适应学习率17)Learning Rate Multiplier - 学习率倍增器18)Learning Rate Reduction - 学习率降低19)Learning Rate Update - 学习率更新20)Scheduled Learning Rate - 定期学习率•Batch Size - 批量大小1)Batch Size - 批量大小2)Mini-Batch - 小批量3)Batch Gradient Descent - 批量梯度下降4)Stochastic Gradient Descent (SGD) - 随机梯度下降5)Mini-Batch Gradient Descent - 小批量梯度下降6)Online Learning - 在线学习7)Full-Batch - 全批量8)Data Batch - 数据批次9)Training Batch - 训练批次10)Batch Normalization - 批量归一化11)Batch-wise Optimization - 批量优化12)Batch Processing - 批量处理13)Batch Sampling - 批量采样14)Adaptive Batch Size - 自适应批量大小15)Batch Splitting - 批量分割16)Dynamic Batch Size - 动态批量大小17)Fixed Batch Size - 固定批量大小18)Batch-wise Inference - 批量推理19)Batch-wise Training - 批量训练20)Batch Shuffling - 批量洗牌•Epoch - 训练周期1)Training Epoch - 训练周期2)Epoch Size - 周期大小3)Early Stopping - 提前停止4)Validation Set - 验证集5)Training Set - 训练集6)Test Set - 测试集7)Overfitting - 过拟合8)Underfitting - 欠拟合9)Model Evaluation - 模型评估10)Model Selection - 模型选择11)Hyperparameter Tuning - 超参数调优12)Cross-Validation - 交叉验证13)K-fold Cross-Validation - K折交叉验证14)Stratified Cross-Validation - 分层交叉验证15)Leave-One-Out Cross-Validation (LOOCV) - 留一法交叉验证16)Grid Search - 网格搜索17)Random Search - 随机搜索18)Model Complexity - 模型复杂度19)Learning Curve - 学习曲线20)Convergence - 收敛3.Machine Learning Techniques and Algorithms (机器学习技术与算法)•Decision Tree - 决策树1)Decision Tree - 决策树2)Node - 节点3)Root Node - 根节点4)Leaf Node - 叶节点5)Internal Node - 内部节点6)Splitting Criterion - 分裂准则7)Gini Impurity - 基尼不纯度8)Entropy - 熵9)Information Gain - 信息增益10)Gain Ratio - 增益率11)Pruning - 剪枝12)Recursive Partitioning - 递归分割13)CART (Classification and Regression Trees) - 分类回归树14)ID3 (Iterative Dichotomiser 3) - 迭代二叉树315)C4.5 (successor of ID3) - C4.5（ID3的后继者）16)C5.0 (successor of C4.5) - C5.0（C4.5的后继者）17)Split Point - 分裂点18)Decision Boundary - 决策边界19)Pruned Tree - 剪枝后的树20)Decision Tree Ensemble - 决策树集成•Random Forest - 随机森林1)Random Forest - 随机森林2)Ensemble Learning - 集成学习3)Bootstrap Sampling - 自助采样4)Bagging (Bootstrap Aggregating) - 装袋法5)Out-of-Bag (OOB) Error - 袋外误差6)Feature Subset - 特征子集7)Decision Tree - 决策树8)Base Estimator - 基础估计器9)Tree Depth - 树深度10)Randomization - 随机化11)Majority Voting - 多数投票12)Feature Importance - 特征重要性13)OOB Score - 袋外得分14)Forest Size - 森林大小15)Max Features - 最大特征数16)Min Samples Split - 最小分裂样本数17)Min Samples Leaf - 最小叶节点样本数18)Gini Impurity - 基尼不纯度19)Entropy - 熵20)Variable Importance - 变量重要性•Support Vector Machine (SVM) - 支持向量机1)Support Vector Machine (SVM) - 支持向量机2)Hyperplane - 超平面3)Kernel Trick - 核技巧4)Kernel Function - 核函数5)Margin - 间隔6)Support Vectors - 支持向量7)Decision Boundary - 决策边界8)Maximum Margin Classifier - 最大间隔分类器9)Soft Margin Classifier - 软间隔分类器10) C Parameter - C参数11)Radial Basis Function (RBF) Kernel - 径向基函数核12)Polynomial Kernel - 多项式核13)Linear Kernel - 线性核14)Quadratic Kernel - 二次核15)Gaussian Kernel - 高斯核16)Regularization - 正则化17)Dual Problem - 对偶问题18)Primal Problem - 原始问题19)Kernelized SVM - 核化支持向量机20)Multiclass SVM - 多类支持向量机•K-Nearest Neighbors (KNN) - K-最近邻1)K-Nearest Neighbors (KNN) - K-最近邻2)Nearest Neighbor - 最近邻3)Distance Metric - 距离度量4)Euclidean Distance - 欧氏距离5)Manhattan Distance - 曼哈顿距离6)Minkowski Distance - 闵可夫斯基距离7)Cosine Similarity - 余弦相似度8)K Value - K值9)Majority Voting - 多数投票10)Weighted KNN - 加权KNN11)Radius Neighbors - 半径邻居12)Ball Tree - 球树13)KD Tree - KD树14)Locality-Sensitive Hashing (LSH) - 局部敏感哈希15)Curse of Dimensionality - 维度灾难16)Class Label - 类标签17)Training Set - 训练集18)Test Set - 测试集19)Validation Set - 验证集20)Cross-Validation - 交叉验证•Naive Bayes - 朴素贝叶斯1)Naive Bayes - 朴素贝叶斯2)Bayes' Theorem - 贝叶斯定理3)Prior Probability - 先验概率4)Posterior Probability - 后验概率5)Likelihood - 似然6)Class Conditional Probability - 类条件概率7)Feature Independence Assumption - 特征独立假设8)Multinomial Naive Bayes - 多项式朴素贝叶斯9)Gaussian Naive Bayes - 高斯朴素贝叶斯10)Bernoulli Naive Bayes - 伯努利朴素贝叶斯11)Laplace Smoothing - 拉普拉斯平滑12)Add-One Smoothing - 加一平滑13)Maximum A Posteriori (MAP) - 最大后验概率14)Maximum Likelihood Estimation (MLE) - 最大似然估计15)Classification - 分类16)Feature Vectors - 特征向量17)Training Set - 训练集18)Test Set - 测试集19)Class Label - 类标签20)Confusion Matrix - 混淆矩阵•Clustering - 聚类1)Clustering - 聚类2)Centroid - 质心3)Cluster Analysis - 聚类分析4)Partitioning Clustering - 划分式聚类5)Hierarchical Clustering - 层次聚类6)Density-Based Clustering - 基于密度的聚类7)K-Means Clustering - K均值聚类8)K-Medoids Clustering - K中心点聚类9)DBSCAN (Density-Based Spatial Clustering of Applications with Noise) - 基于密度的空间聚类算法10)Agglomerative Clustering - 聚合式聚类11)Dendrogram - 系统树图12)Silhouette Score - 轮廓系数13)Elbow Method - 肘部法则14)Clustering Validation - 聚类验证15)Intra-cluster Distance - 类内距离16)Inter-cluster Distance - 类间距离17)Cluster Cohesion - 类内连贯性18)Cluster Separation - 类间分离度19)Cluster Assignment - 聚类分配20)Cluster Label - 聚类标签•K-Means - K-均值1)K-Means - K-均值2)Centroid - 质心3)Cluster - 聚类4)Cluster Center - 聚类中心5)Cluster Assignment - 聚类分配6)Cluster Analysis - 聚类分析7)K Value - K值8)Elbow Method - 肘部法则9)Inertia - 惯性10)Silhouette Score - 轮廓系数11)Convergence - 收敛12)Initialization - 初始化13)Euclidean Distance - 欧氏距离14)Manhattan Distance - 曼哈顿距离15)Distance Metric - 距离度量16)Cluster Radius - 聚类半径17)Within-Cluster Variation - 类内变异18)Cluster Quality - 聚类质量19)Clustering Algorithm - 聚类算法20)Clustering Validation - 聚类验证•Dimensionality Reduction - 降维1)Dimensionality Reduction - 降维2)Feature Extraction - 特征提取3)Feature Selection - 特征选择4)Principal Component Analysis (PCA) - 主成分分析5)Singular Value Decomposition (SVD) - 奇异值分解6)Linear Discriminant Analysis (LDA) - 线性判别分析7)t-Distributed Stochastic Neighbor Embedding (t-SNE) - t-分布随机邻域嵌入8)Autoencoder - 自编码器9)Manifold Learning - 流形学习10)Locally Linear Embedding (LLE) - 局部线性嵌入11)Isomap - 等度量映射12)Uniform Manifold Approximation and Projection (UMAP) - 均匀流形逼近与投影13)Kernel PCA - 核主成分分析14)Non-negative Matrix Factorization (NMF) - 非负矩阵分解15)Independent Component Analysis (ICA) - 独立成分分析16)Variational Autoencoder (VAE) - 变分自编码器17)Sparse Coding - 稀疏编码18)Random Projection - 随机投影19)Neighborhood Preserving Embedding (NPE) - 保持邻域结构的嵌入20)Curvilinear Component Analysis (CCA) - 曲线成分分析•Principal Component Analysis (PCA) - 主成分分析1)Principal Component Analysis (PCA) - 主成分分析2)Eigenvector - 特征向量3)Eigenvalue - 特征值4)Covariance Matrix - 协方差矩阵。
人工智能原理_北京大学中国大学mooc课后章节答案期末考试题库2023年1.Turing Test is designed to provide what kind of satisfactory operationaldefinition?图灵测试旨在给予哪一种令人满意的操作定义？答案:machine intelligence 机器智能2.Thinking the differences between agent functions and agent programs, selectcorrect statements from following ones.考虑智能体函数与智能体程序的差异，从下列陈述中选择正确的答案。
答案:An agent program implements an agent function.一个智能体程序实现一个智能体函数。
3.There are two main kinds of formulation for 8-queens problem. Which of thefollowing one is the formulation that starts with all 8 queens on the boardand moves them around?有两种8皇后问题的形式化方式。
“初始时8个皇后都放在棋盘上，然后再进行移动”属于哪一种形式化方式？答案:Complete-state formulation 全态形式化4.What kind of knowledge will be used to describe how a problem is solved?哪种知识可用于描述如何求解问题？答案:Procedural knowledge 过程性知识5.Which of the following is used to discover general facts from trainingexamples?下列中哪个用于训练样本中发现一般的事实？答案:Inductive learning 归纳学习6.Which statement best describes the task of “classification” in machinelearning?哪一个是机器学习中“分类”任务的正确描述？答案:To assign a category to each item. 为每个项目分配一个类别。
参考文献(人工智能)曹晖目的：对参考文献整理（包括摘要、读书笔记等），方便以后的使用。
分类：粗分为论文（paper）、教程（tutorial）和文摘（digest）。
0介绍 (1)1系统与综述 (1)2神经网络 (2)3机器学习 (2)3.1联合训练的有效性和可用性分析 (2)3.2文本学习工作的引导 (2)3.3★采用机器学习技术来构造受限领域搜索引擎 (3)3.4联合训练来合并标识数据与未标识数据 (5)3.5在超文本学习中应用统计和关系方法 (5)3.6在关系领域发现测试集合规律性 (6)3.7网页挖掘的一阶学习 (6)3.8从多语种文本数据库中学习单语种语言模型 (6)3.9从因特网中学习以构造知识库 (7)3.10未标识数据在有指导学习中的角色 (8)3.11使用增强学习来有效爬行网页 (8)3.12★文本学习和相关智能A GENTS：综述 (9)3.13★新事件检测和跟踪的学习方法 (15)3.14★信息检索中的机器学习——神经网络，符号学习和遗传算法 (15)3.15用NLP来对用户特征进行机器学习 (15)4模式识别 (16)4.1JA VA中的模式处理 (16)0介绍1系统与综述2神经网络3机器学习3.1 联合训练的有效性和可用性分析标题：Analyzing the Effectiveness and Applicability of Co-training链接：Papers 论文集\AI 人工智能\Machine Learning 机器学习\Analyzing the Effectiveness and Applicability of Co-training.ps作者：Kamal Nigam, Rayid Ghani备注：Kamal Nigam (School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, knigam@)Rayid Ghani (School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213 rayid@)摘要：Recently there has been significant interest in supervised learning algorithms that combine labeled and unlabeled data for text learning tasks. The co-training setting [1] applies todatasets that have a natural separation of their features into two disjoint sets. We demonstrate that when learning from labeled and unlabeled data, algorithms explicitly leveraging a natural independent split of the features outperform algorithms that do not. When a natural split does not exist, co-training algorithms that manufacture a feature split may out-perform algorithms not using a split. These results help explain why co-training algorithms are both discriminativein nature and robust to the assumptions of their embedded classifiers.3.2 文本学习工作的引导标题：Bootstrapping for Text Learning Tasks链接：Papers 论文集\AI 人工智能\Machine Learning 机器学习\Bootstrap for Text Learning Tasks.ps作者：Rosie Jones, Andrew McCallum, Kamal Nigam, Ellen Riloff备注：Rosie Jones (rosie@, 1 School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213)Andrew McCallum (mccallum@, 2 Just Research, 4616 Henry Street, Pittsburgh, PA 15213)Kamal Nigam (knigam@)Ellen Riloff (riloff@, Department of Computer Science, University of Utah, Salt Lake City, UT 84112)摘要：When applying text learning algorithms to complex tasks, it is tedious and expensive to hand-label the large amounts of training data necessary for good performance. This paper presents bootstrapping as an alternative approach to learning from large sets of labeled data. Instead of a large quantity of labeled data, this paper advocates using a small amount of seed information and alarge collection of easily-obtained unlabeled data. Bootstrapping initializes a learner with the seed information; it then iterates, applying the learner to calculate labels for the unlabeled data, and incorporating some of these labels into the training input for the learner. Two case studies of this approach are presented. Bootstrapping for information extraction provides 76% precision for a 250-word dictionary for extracting locations from web pages, when starting with just a few seed locations. Bootstrapping a text classifier from a few keywords per class and a class hierarchy provides accuracy of 66%, a level close to human agreement, when placing computer science research papers into a topic hierarchy. The success of these two examples argues for the strength of the general bootstrapping approach for text learning tasks.3.3 ★采用机器学习技术来构造受限领域搜索引擎标题：Building Domain-specific Search Engines with Machine Learning Techniques链接：Papers 论文集\AI 人工智能\Machine Learning 机器学习\Building Domain-Specific Search Engines with Machine Learning Techniques.ps作者：Andrew McCallum, Kamal Nigam, Jason Rennie, Kristie Seymore备注：Andrew McCallum (mccallum@ , Just Research, 4616 Henry Street Pittsburgh, PA 15213)Kamal Nigam (knigam@ , School of Computer Science, Carnegie Mellon University Pittsburgh, PA 15213)Jason Rennie (jr6b@)Kristie Seymore (kseymore@)摘要：Domain-specific search engines are growing in popularity because they offer increased accuracy and extra functionality not possible with the general, Web-wide search engines. For example, allows complex queries by age-group, size, location and cost over summer camps. Unfortunately these domain-specific search engines are difficult and time-consuming to maintain. This paper proposes the use of machine learning techniques to greatly automate the creation and maintenance of domain-specific search engines. We describe new research in reinforcement learning, information extraction and text classification that enables efficient spidering, identifying informative text segments, and populating topic hierarchies. Using these techniques, we have built a demonstration system: a search engine forcomputer science research papers. It already contains over 50,000 papers and is publicly available at ....采用多项Naive Bayes 文本分类模型。
光学 精密工程Optics and Precision Engineering第 29 卷 第 5 期2021年5月Vol. 29 No. 5May 2021文章编号 1004-924X( 2021)05-1127-09联合训练生成对抗网络的半监督分类方法徐哲，耿杰*，蒋雯，张卓，曾庆捷(西北工业大学电子信息学院，西安710072)摘要：深度神经网络需要大量数据进行监督训练学习，而实际应用中往往难以获取大量标签数据°半监督学习可以减小深度网络对标签数据的依赖，基于半监督学习的生成对抗网络可以提升分类效果,旦仍存在训练不稳定的问题°为进一步提高网络的分类精度并解决网络训练不稳定的问题，本文提出一种基于联合训练生成对抗网络的半监督分类方法，通 过两个判别器的联合训练来消除单个判别器的分布误差，同时选取无标签数据中置信度高的样本来扩充标签数据集，提高半监督分类精度并提升网络模型的泛化能力°在CIFAR -10和SVHN 数据集上的实验结果表明，本文方法在不同数量的标签数据下都获得更好的分类精度°当标签数量为2 000时，在CIFAR -10数据集上分类精度可达80.36% ;当标签 数量为10时，相比于现有的半监督方法，分类精度提升了约5%°在一定程度上解决了 GAN 网络在小样本条件下的过拟合问题°关键词：生成对抗网络；半监督学习；图像分类；深度学习中图分类号:TP391文献标识码：Adoi ：10. 37188/OPE. 20212905.1127Co -training generative adversarial networks forsemi -supervised classification methodXU Zhe , GENG Jie * , JIANG Wen , ZHANG Zhuo , ZENG Qing -jie(School of E lectronics and Information , Northwestern Polytechnical University , Xian 710072, China )* Corresponding author , E -mail ： gengjie@nwpu. edu. cnAbstract ： Deep neural networks require a large amount of data for supervised learning ； however , it is dif ficult to obtain enough labeled data in practical applications. Semi -supervised learning can train deep neuralnetworks with limited samples. Semi -supervised generative adversarial networks can yield superior classifi cation performance ； however ， they are unstable during training in classical networks. To further improve the classification accuracy and solve the problem of training instability for networks ， we propose a semi -su pervised classification model called co -training generative adversarial networks ( CT -GAN ) for image clas sification. In the proposed model ， co -training of two discriminators is applied to eliminate the distribution error of a single discriminator and unlabeled samples with higher confidence are selected to expand thetraining set , which can be utilized for semi -supervised classification and enhance the generalization of deep networks. Experimental results on the CIFAR -10 dataset and the SVHN dataset showed that the pro posed method achieved better classification accuracies with different numbers of labeled data. The classifi cation accuracy was 80. 36% with 2000 labeled data on the CIFAR -10 dataset , whereas it improved by收稿日期：2020-11-04；修订日期:2021-01-04.基金项目:装备预研领域基金资助项目(No. 61400010304);国家自然科学基金资助项目(No. 61901376)1128光学精密工程第29卷about5%compared with the existing semi-supervised method with10labeled data.To a certain extent, the problem of GAN overfitting under a few sample conditions is solved.Key words：generative adversarial networks；semi-supervised learning；image classification；deep learning1引言图像分类作为计算机视觉领域最基础的任务之一，主要通过提取原始图像的特征并根据特征学习进行分类［11o传统的特征提取方法主要是对图像的颜色、纹理、局部特征等图像表层特征进行处理实现的，例如尺度不变特征变换法［21,方向梯度法［31以及局部二值法［41等。
SAP全套课程/标准教材/培训教材之编号和名称及下载地址(将陆续补上)Course Code课程编号 Solution Type所属模块 Course Title课程名称AC010 Financials Business Processes in Financial Accounting (includes e-learning SAP125 SAP Navigation )下载地址：ShowPost.asp?ThreadID=1305AC020 Financials Processes in Investment ManagementAC040 Financials Business Processes in Management Accounting (includes e-learningSAP125 SAP Navigation)AC050 Financials Business Processes in Financial and Management Accounting with the New Gene ral Ledger(includes e-learningSAP125 SAP Navigation)AC200 Financials Financial Accounting Customizing I: General Ledger, Accounts Payable, Accou nts ReceivableAC201 Financials Payment and Dunning Program, Correspondence, Interest CalculationAC202 Financials Financial Accounting Customizing II: Special G/L Transactions, Document Par king, Validation and SubstitutionAC205 Financials Financial ClosingAC206 Financials Parallel Valuation and Financial Reporting: Local Law – IAS (IFRS) / US- G AAPAC210 Financials New General LedgerAC212 Financials Migration to the New General LedgerAC220 Financials Special Purpose LedgerAC270 Financials Travel Management - Travel ExpensesAC280 Financials Analytics & Reporting in Financial AccountingAC290 Financials Real Estate ManagementAC295 Financials Flexible Real Estate ManagementAC305 Financials Asset AccountingAC350 Financials System Configuration for Investment ManagementAC400 Financials Programming in FinancialsAC405 Financials Cost Center and Internal Order AccountingAC412 Financials Cost Centre Accounting: Extended FunctionalityAC420 Financials Template Allocation Procedure for Processes and ActivitiesAC505 Financials Product Cost PlanningAC520 Financials Controlling for Make to Order / Stock ProductionAC530 Financials Actual Costing/Material LedgerAC605 Financials Profitability AnalysisAC610 Financials Profit Centre AccountingAC650 Financials Transfer PricesAC660 Financials EC-CS Consolidation FunctionsAC665 Financials EC-CS Integrated ConsolidationAC680 Financials Analytics & Reporting in Management AccountingAC805 Financials Cash ManagementAC990 Financials Tips and Tricks in Management Accounting (from SAP R/3 4.6 to ECC 6.0)ADM100 NetWeaver - Sys Admin SAP Web AS Administration IADM102 NetWeaver - Sys Admin SAP Web AS Administration IIADM106 NetWeaver - Sys Admin SAP System Monitoring Using CCMS IADM107 NetWeaver - Sys Admin Advanced SAP System Monitoring using CCMS IIADM110 NetWeaver - Sys Admin SAP ECC 5.0 InstallationADM200 NetWeaver - Sys Admin SAP Web Application Server Java Administration (Note: This cour se covers the content of TADMD5)ADM225 NetWeaver - Sys Admin SAP Software Logistics for JavaADM315 NetWeaver - Sys Admin Workload AnalysisADM325 NetWeaver - Sys Admin Software LogisticsADM326 NetWeaver - Sys Admin SAP ECC UpgradeADM505 NetWeaver - Sys Admin Oracle Database Administration IADM506 NetWeaver - Sys Admin Oracle Database Administration IIADM515 NetWeaver - Sys Admin SAP DB Database AdministrationADM520 NetWeaver - Sys Admin Database Administration MS SQL ServerADM535 NetWeaver - Sys Admin DB2 UDB (UNIX and Windows) Database AdministrationADM940 NetWeaver - Sys Admin SAP R/3 Authorization ConceptADM950 NetWeaver - Sys Admin Secure SAP System ManagementADM960 NetWeaver - Sys Admin Security in SAP System EnvironmentsANA10 NetWeaver - BI SAP xApp Analytics using SAP NetWeaver Visual ComposerBC400 NetWeaver - Programming ABAP Workbench FoundationsBC401 NetWeaver - Programming ABAP ObjectsBC402 NetWeaver - Programming Advanced ABAPBC405 NetWeaver - Programming ABAP ReportingBC407 NetWeaver - Programming Reporting: QuickViewes, InfoSet Query and SAP QueryBC408 NetWeaver - Programming ABAP lists: Processing data using Extracts (eLearning)BC410 NetWeaver - Programming Programming User DialogsBC412 NetWeaver - Programming ABAP Dialogs Programming using Enjoy SAP ControlsBC414 NetWeaver - Programming Programming Database ChangesBC415 NetWeaver - Programming Remote Function Calls in ABAPBC416 NetWeaver - Programming ABAP Web ServicesBC417 NetWeaver - Programming BAPI Development for Accessing SAPBC420 NetWeaver - Programming Data TransferBC425 NetWeaver - Programming Enhancements and ModificationsBC430 NetWeaver - Programming ABAP DictionaryBC460 NetWeaver - Programming SAPscript: Forms Design and Text ManagementBC470 NetWeaver - Programming Form Printing with SAP Smart FormsBC480 NetWeaver - Programming PDF-Based Print FormsBIT100 NetWeaver - BIT SAP NetWeaver Process Integration – Overview (includes e-learning co urse SAP125 SAP Navigation)BIT140 NetWeaver - BIT XML IntroductionBIT300 NetWeaver - BIT Integration Technology ALEBIT350 NetWeaver - BIT Application Link Enabling (ALE) EnhancementsBIT400 NetWeaver - XI SAP Exchange InfrastructureBIT402 NetWeaver - XI XI - Adapter Concepts (File, JDBC, JMS, Mail) - eLearningBIT403 NetWeaver - XI XI - Adapter Concepts (Plain HTTP, IDoc, RFC, SOAP, PCK) - eLearning BIT430 NetWeaver - XI SAP XI Business Process ManagementBIT450 NetWeaver - XI SAP Exchange Infrastructure DevelopmentBIT460 NetWeaver - XI SAP Exchange Infrastructure MappingBIT526 NetWeaver - BIT Developing BAPI-Enabled Web Applications with JavaBIT528 NetWeaver - BIT SAP .NET Connector ProgrammingBIT530 NetWeaver - BIT SAP Business Connector: IntroductionBIT531 NetWeaver - BIT SAP Business Connector IntegrationBIT600 NetWeaver - BIT SAP Business Workflow - Concepts, Inboxes, Reporting and Template Usa ge (Normally offered as a 2 day course, Australia offering it as 1.5 days)See also workshop WAUBIT (both BIT600 and BIT601)BIT601 NetWeaver - BIT SAP Business Workflow - Build and Use (Normally offered as 5 days cou rse, Australia offering it as 3.5 days)See also workshop WAUBIT (both BIT600 and BIT601)BIT603 NetWeaver - BIT SAP Business Workflow and Web ScenariosBIT610 NetWeaver - BIT SAP Business Workflow - ProgrammingBIT615 NetWeaver - BIT SAP Archive Link Document Management with SAP Archive LinkBIT640 NetWeaver - BIT SAP NetWeaver - SAP Records Management in DetailBIT660 NetWeaver - BIT Data ArchivingBIT670 NetWeaver - BIT ADK - Retrieval ProgrammingBPERP All-in-One SAP Best Practices - Integrated business processes based on SAP R/3 Enterpr iseBPTAA All-in-One SAP Best Practices Tools and AcceleratorsBPTAQ All-in-One SAP Best Practices Tools and Accelerators (Lean Baseline)BW001 NetWeaver - BI SAP NetWeaver Business IntelligenceBW305 NetWeaver - BI BI - Enterprise Reporting, Query & Analysis (Part I)BW306 NetWeaver - BI BI - Enterprise Reporting, Query & Analysis (Part II)BW310 NetWeaver - BI BI - Enterprise Data Warehousing ( includes e-learning courses SAP125 & SAP130)BW330 NetWeaver - BI BI - Modelling & ImplamentationBW350 NetWeaver - BI BI - Data AcquisitionBW360 NetWeaver - BI BI - Performance & AdministrationBW365 NetWeaver - BI BI - User Management & AuthorizationBW370 NetWeaver - BI BI - Integrated PlanningBW380 NetWeaver - BI BI - Analysis Processes & Data MiningCA500 HCM Cross Application Time SheetCA611 NetWeaver - Web AS Test Management with eCATTCA705 Financials Basics of the Report Painter/Report WriterCA710 Financials Advanced Functions of the Report WriterCR100 CRM CRM Customising FundamentalsCR300 CRM CRM SalesCR310 CRM SAP Mobile Application StudioCR400 CRM CRM Interaction Centre WinClientCR410 CRM CRM Interaction Centre WebClientCR500 CRM CRM MiddlewareCR600 CRM CRM MarketingCR700 CRM CRM ServiceCR800 CRM CRM E-CommerceCR900 CRM Analytical CRMCRM001 CRM Empower Sales, Services, and Marketing with SAPCRM SolutionD30BW NetWeaver - BI Business Information Warehouse (BW) - SAP BW Delta 3.0D346AW NetWeaver - Programming ABAP Workbench Delta Course 3.x to 4.6D620AW NetWeaver - Programming Delta ABAP Workbench SAP R/3 4.6C-SAP Web Application Server 6.2DBITWF NetWeaver - BIT SAP Workflow - Delta R/3 Enterprise on SAP NetWeaver 2004sDBW70E NetWeaver - BI BI - Delta Enterprise Data Warehousing SAP NetWeaver 2004sDBW70P NetWeaver - BI BI - Delta Planning SAP NetWeaver 2004sDBW70R NetWeaver - BI BI - Delta Reporting SAP NetWeaver 2004sDERPAA Financials Delta SAP System in Asset AccountingDERPFA SCM Delta SAP Enterprise in Production OrdersDERPFI Financials Delta SAP System in Financial AccountingDERPHR HCM Delta in SAPERP Human Capital ManagementDERPLD Life-Cycle Data Management Delta ERP 2004 Life-Cycle Data ManagementDERPLE SCM DERPLE Delta Warehouse Management / TransportationDERPMM SCM Delta SAP R/3 3.1 — ERP2004 Materials ManagementDERPOM Financials Delta SAP System in CO-OMDERPPA Financials Delta SAP System in Profitability AnalysisDERPPC Financials Delta SAP System in Product Cost ControllingDERPPL SCM Delta Production PlanningDERPPM Life-Cycle Data Management Delta SAP R/3 Enterprise in Plant Maintenance/Customer Ser viceDERPPS Life-Cycle Data Management Delta SAP R/3 Enterprise in PSDERPQM Life-Cycle Data Management Delta ERP 2004 in Quality ManagementDERPRM Life-Cycle Data Management Delta Repetitive ManufacturingDERPSP SCM Delta Sales Order ManagementDUT010 Duet Installation and Administration of DuetEH101 Life-Cycle Data Management EH&S OverviewEH102 Life-Cycle Data Management Basic Data and Tools (BAS)EH102a Life-Cycle Data Management BAS ProfessionalEH202 Life-Cycle Data Management Product SafetyEH202a Life-Cycle Data Management PS ProfessionalEH252 Life-Cycle Data Management Global Label Management (GLM)EH252a Life-Cycle Data Management GLM ProfessionalEH302 Life-Cycle Data Management Dangerous GoodsEH302a Life-Cycle Data Management DG ProfessionalEH402 Life-Cycle Data Management Industrial Hygiene/SafetyEH402a Life-Cycle Data Management HIS ProfessionalEH502 Life-Cycle Data Management Occupational HealthEH602 Life-Cycle Data Management Waste ManagementEH602a Life-Cycle Data Management WA ProfessionalEH702 Life-Cycle Data Management Hazardous Substance Management (HSM)EH912 Life-Cycle Data Management WWI Layout (WWI)EH912a Life-Cycle Data Management WWI ProfessionalEH920 Life-Cycle Data Management Customising Product Safety/Dangerous Goods Management EH930 Life-Cycle Data Management EH&S ExpertEP120 NetWeaver - EP SAP NetWeaver Portal DevelopmentEP130 NetWeaver - EP SAP Knowledge Management and Collaboration DevelopmentEP150 NetWeaver - EP SAP Enterprise Portal and KMC DevelopmentEP200 NetWeaver - EP SAP Enterprise Portal System AdministrationEP300 NetWeaver - EP Configuration of Knowledge Management and CollaborationEP600 NetWeaver - EP Configuration of the Universal WorklistERP001 Overview Management Empowered by SAPERPERP020 Overview Management Empowered by SAPERP FinancialsERP030 Overview Management Empowered by SAPERP Human Capital ManagementERP040 Overview Management Empowered by SAPERP Logistics & OperationsERP200 Role Based E-Learning CO OverviewERP201 Role Based E-Learning Controlling Master DataERP202 Role Based E-Learning Cost Center Planning and PostingsERP203 Role Based E-Learning Cost Center Period-end Closing and ReportsERP204 Role Based E-Learning Internal OrderERP205 Role Based E-Learning Product CostERP206 Role Based E-Learning Control Order-Production OrderERP207 Role Based E-Learning Profit CenterERP250 Role Based E-Learning FI OverviewERP251 Role Based E-Learning General Ledger Overview and Master DataERP252 Role Based E-Learning General LedgerERP253 Role Based E-Learning Billing OverviewERP254 Role Based E-Learning Accounts ReceivableERP255 Role Based E-Learning Financial Master DataERP256 Role Based E-Learning Accounts PayableERP257 Role Based E-Learning Cash ManagementERP258 Role Based E-Learning Financial ReportsERP259 Role Based E-Learning Asset Accounting OverviewERP260 Role Based E-Learning Asset Accounting Master DataERP261 Role Based E-Learning Asset Accounting DetailsERP262 Role Based E-Learning Asset Accounting ClosingERP270 Role Based E-Learning Travel Management OverviewERP271 Role Based E-Learning Travel ManagementERP280 Role Based E-Learning Funds Management (Funds Manager)ERP400 Role Based E-Learning MM OverviewERP401 Role Based E-Learning Material and Service Master DataERP402 Role Based E-Learning Purchasing OverviewERP403 Role Based E-Learning Vendor and Purchasing Information Master Data and Source List ERP404 Role Based E-Learning Purchasing Requisitions, Quotations & ContractsERP405 Role Based E-Learning MM ReportsERP406 Role Based E-Learning Inventory ManagementERP407 Role Based E-Learning MRP and Physical InventoryERP408 Role Based E-Learning Logistics Invoice VerificationERP450 Role Based E-Learning SD OverviewERP451 Role Based E-Learning Sales Order ProcessingERP452 Role Based E-Learning Customer Master DataERP453 Role Based E-Learning Quotation ManagementERP454 Role Based E-Learning Scheduling Agreements and ContractsERP455 Role Based E-Learning SD ReportsERP456 Role Based E-Learning Pricing Condition RecordsERP457 Role Based E-Learning Sales AgreementsERP458 Role Based E-Learning Backorder ProcessingERP459 Role Based E-Learning Outbound ProcessingERP460 Role Based E-Learning Billing ProcessERP461 Role Based E-Learning A/R and Credit ManagementERP462 Role Based E-Learning Credit and Risk ManagementERP463 Role Based E-Learning Credit ReportingERP900 Role Based E-Learning Order to Cash Overview IERP901 Role Based E-Learning Sales PersonERP902 Role Based E-Learning Sales Administrator IERP903 Role Based E-Learning Sales Administrator IIERP904 Role Based E-Learning Outbound Shipping ClerkERP905 Role Based E-Learning Billing EmployeeERP906 Role Based E-Learning Credit Analyst IERP907 Role Based E-Learning Credit Analyst IIERP910 Role Based E-Learning Procure to Pay Overview IERP912 Role Based E-Learning Requisition Clerk & Invoice Verification Clerk IERP913 Role Based E-Learning Invoice Verification Clerk IIERP914 Role Based E-Learning BuyerERP915 Role Based E-Learning Receiving ClerkERP920 Role Based E-Learning Controlling Process Overview IERP921 Role Based E-Learning Cost Center Analyst and Controller IERP922 Role Based E-Learning Internal Order AnalystERP923 Role Based E-Learning Product Cost Analyst IERP924 Role Based E-Learning Profitability AnalystERP925 Role Based E-Learning Product Cost Analyst II and Controller IIERP926 Role Based E-Learning Controller IIIERP930 Role Based E-Learning Financial Process Overview IERP931 Role Based E-Learning Accounting AnalystERP932 Role Based E-Learning Accounts Receivable and Accounts Payable Clerk IERP933 Role Based E-Learning Travel ManagerERP934 Role Based E-Learning Asset Manager IERP935 Role Based E-Learning Asset Manager IIERP936 Role Based E-Learning Accounts Receivable Clerk IIERP937 Role Based E-Learning Accounts Payable Clerk IIESA100 NetWeaver - ESA Enterprise Services Architecture (ESA) FundamentalsE2E050 Solution Manager E2E Solution Scope and DocumentationE2E300 Solution Manager E2E Solution Support - Integration & AutomationE2E400 Solution Manager E2E Technical Upgrade ManagementFIN009 Financials Corporate Governance Overview (Auditing and Sarbanes-Oxley Act)FIN090 Financials SAP Solution Overview for Auditing and the Sarbanes-Oxley ActFIN900 Financials Auditing of Financial Business Process in SAPFIN910 Financials Management of Internal ControlsFIN930 Financials Auditing with SAP Business Warehouse & Strategic Enterprise Management (BW /SEM)FS200 Industry SAP Banking OverviewFS210 Industry Loans Management for Financial ServicesFS220 Industry Credit Risk AnalyserFS225 Industry Collateral ManagementFS230 Industry Bank Customer AccountsFS240 Industry Profit AnalyserFS250 Industry Market Risk AnalyserFS251 Industry Asset Liability ManagementFS290 Industry Financial DatabaseFS291 Industry SAP Accounting for Financial InstrumentsFS300 Industry SAP for Insurance - OverviewFS310 Industry SAP Collections and Disbursements: OverviewFS315 Industry SAP Collections and Disbursements CustomizingFS320 Industry Incentive & Commission ManagementFS330 Industry SAP Claims Management - OverviewFS335 Industry SAP Claims Management - CustomisingFSC010 Financials Business Processes in Treasury and Risk ManagementFSC020 Financials Business Processes in SAP Credit Management, SAP Biller Direct, SAP Disput e and SAP Collections ManagementFSC120 Financials SAP In-House CashGRC200 Financials Manage Compliance with SAP E-Learning: Introduction to Virsa Compliance Ca librator v5.1GRC220 Financials Compliant Provisioning with SAP E-Learning: Introduction to Virsa Access E nforcer v5.1GTS100 SCM SAP Global Trade Services OverviewHR050 HCM Business Processes in Human Capital ManagementHR110 HCM Essentials of PayrollHR130 HCM Essentials of SAP Enterprise Portal in HCMHR250 HCM Employee Self-ServiceHR260 HCM Manager Self-ServiceHR270 HCM SAP Learning Solution OverviewHR275 HCM eLearning with SAP TutorHR290 HCM System Configuration for ESS/MSSHR305 HCM Configuration of Master DataHR306 HCM Configuration of Time RecordingHR310 HCM Time Evaluation with Clock TimesHR311 HCM Time Evaluation Without Clock TimesHR315 HCM RecruitmentHR316 HCM E-RecruitingHR325 HCM Benefits AdministrationHR350 HCM Programming in Human Capital ManagementHR400 HCM Payroll ConfigurationHR413 HCM Australian PayrollHR505 HCM Organiational ManagementHR506 HCM Advanced Organizational ManagementHR510 HCM Personnel DevelopmentHR515 HCM Training and Event ManagementHR540 HCM Enterprise Compensation ManagementHR550 HCM Personnel Cost Planning and SimulationHR580 HCM Analytics and Reporting in HCMHR940 HCM Authorisations in HCMHR990 HCM Technical Tips and Tricks in HCMIAU210 Industry SAPAutomotive: Processes in the Supplier IndustryIAU240 Industry SAPAutomotive: JIT ProcessesIAU260 Industry SAP for Automotive: Operational Procurement and Material Flow IAU290 Industry SAP for Automotive: Dealer Business ManagementICP310 Industry Sales & DistributionICP320 Industry Materials Management and Production PlanningICP500 Industry Beverage ProcessesIEG110 Industry Workshop: E-Government - Web RequestsIHE102 Industry SAP Campus Management: Managing a Student Life CycleIHE103 Industry Campus Management Workshop Configuration and ToolsIHE203 Industry Campus Management Student AccountingIMD320 Industry SAP for Media - Periodical Sales and DistributionIMD420 Industry SAP for Media - Advertising Management (Publishers)IMD500 Industry SAP for Media: Media Product Sales and DistributionIMD700 Industry SAP for Media – Intellectual Property ManagementIOG130 Industry Oil & Gas Industry Supply ChainIOG150 Industry Oil & Gas Industry Supply ChainIOG320 Industry Oil & Gas Exchanges BusinessIOG330 Industry Bulk Transportation and Shipment CostingIOG340 Industry Bulk SchedulingIOG350 Industry Service Station RetailingIOGW40 Industry SAP Joint Venture Accounting (JVA)IOGW50 Industry Remote Logistics management (RLM)IOGW60 Industry Production Sharing Accounting with SAP PSAIPS030 Industry PBC Commitment ProcessorIPS050 Industry PBC Organisation of Public ServicesIPS510 Industry SAP Public Sector Collection and DisbursementIPS640 Industry SAP Public Sector Records ManagementIPS810 Industry SAP Grants Management/ GranteeIPS910 Industry Funds Management: Processes, Organisation and Configuration SAPIRT Industry Retail Process OverviewIRT310 Industry Master Data in SAP for RetailIRT320 Industry Pricing and PromotionsIRT330 Industry Requirements/Planning/PurchasingIRT340 Industry Supply Chain ExecutionIRT360 Industry Store ConnectionIRT370 Industry SAP Retail StoreIUT110 Industry Introduction to IS-U/CCSIUT210 Industry Master Data and Basic FunctionsIUT220 Industry Device ManagementIUT221 Industry Work ManagementIUT225 Industry Energy Data ManagementIUT230 Industry Billing and InvoicingIUT235 Industry Real-Time PricingIUT240 Industry Contract Accounts: Recievable & PayableIUT250 Industry Customer ServiceIUT255 Industry Integration of SAP CRM and SAP IS-UJA100 NetWeaver - Web AS SAP J2SE FundamentalsJA200 NetWeaver - Web AS Java GUI KitJA300 NetWeaver - Web AS SAP J2EE Start-up KitJA310 NetWeaver - Web AS Java Web Dynpro BasicsJA312 NetWeaver - Web AS Advanced Java Web DynproJA313 NetWeaver - Web AS Java WebDynpro - Adobe FormsJA314 NetWeaver - Web AS Java WebDynpro - Business GraphicsJA320 NetWeaver - Web AS SAP Java Persistence FrameworkJA331 NetWeaver - Web AS SAP Java Open Integration TechnologiesJA340 NetWeaver - Web AS SAP NetWeaver Development InfrastructureMDM100 NetWeaver - MDM Master Data Management 5.5 SP04MDM101 NetWeaver - MDM Global Data SynchronisationMDM300 NetWeaver - MDM Master Data Management 5.5 SP04 Print PublishingMDM400 NetWeaver - MDM SAP NetWeaver Data Modeling in MDMNET200 NetWeaver - Web AS SAP Web Application Server: BSP Application Development NET310 NetWeaver - Web AS ABAP Web DynproNET311 NetWeaver - Web AS Advanced Web Dynpro for ABAPNW001 Overview Technology Solutions Powered by SAP NetWeaverOBA31S Industry SAP Bank Analyser 3OBW31S NetWeaver - BIT SAP BW 3.1COBW35S NetWeaver - BIT SAP BW 3.5OCB13S Industry SAP Core BankingOCD47S Industry SAP Coll. and Disbursements 4.72OCL47S Industry SAP Claims Management 4.72OCM41S Industry SAP Campus Management 4.71OCM42S Industry SAP Campus Management 4.72OCP20S Life-Cycle Data Management SAP cProject Suite 2.0OCP30S Life-Cycle Data Management SAP cProject Suite 3.0OCP31S Life-Cycle Data Management SAP cProject Suite 3.1OCR31S CRM SAP CRM 3.1OCR40S CRM Online Knowledge Product SAP CRM 4.0OCR50S CRM SAPCRM 2005OCR60S CRM SAPCRM 2006sODIMP5 Industry SAP DIMP 5.0ODIMPS Industry SAP DIMP 4.71OE4CS Industry SAPERP2004 - Corporate Services: Travel & Real Estate MgtOE4DSD Industry SAPERP2004 - Direct Store Delivery & BeverageOE4FIN Financials SAPERP 2004 - FinancialsOE4FS Financials SAPERP2004 - Financial ServicesOE4HCM HCM SAPERP2004 - Human Capital ManagementOE4OPS Industry SAPERP2004 - Operations / LogisticsOE4PS Industry SAPERP2004 - Public ServicesOE4SEM Industry SAPERP2004 - Analytics / SEMOE5CS Industry Corporate Services (Travel, Real Estate, Project Portfolio, Quality Mgmt. & E HS)OE5FIN Financials FinancialsOE5FS Financials Financial Services Industries (Banking, Insurance, FSCM)OE5HCM HCM Human Capital ManagementOE5MI Industry Manufacturing Industries (Catch Weight Mgmt., Chemicals, Mining, Oil & Gas) OE5PRC Industry ProcurementOE5PRD Industry Manufacturing Execution, Product Development & Enterprise Asset Management OE5PS Industry Public ServicesOE5RET Industry Retail / TradingOE5SEM Industry Analytics / SEMOE5SI Industry Service Industries (Media, Prof. Services, Telecommunications, Utilities) OE5SLS Industry Sales, Incentive & Commission ManagementOEP50S Netweaver - EP SAP Enterprise Portal 5.0OEP60S Netweaver - EP SAP Enterprise Portal 6.0 SP02OEP64S Netweaver - EP SAP EP 6.0 on SAP Web AS 6.40OERP4S Overview SAPERP 2004OFS20S Financials SAP FSCM 2.0OGT20S NetWeaver - Web AS SAP GTS 2.0OGT30S NetWeaver - Web AS SAP GTS 3.0OGT70S NetWeaver - Web AS SAP GTS 7.0OIN42S Industry SAP Reinsurance 4.72OLS20S HCM SAP Learning Solution 2.0OMA47S Industry SAP Media 4.72OMB03S Netweaver - MI SAP Mobile BusinessOMD55S Netweaver - MDM SAP MDM 5.5OME21S NetWeaver - MI SAP Mobile Engine 2.1OME25S Netweaver - MI SAP Mobile Infrastructure 2.5OMGD1S Netweaver - MI SAP MDM (GDS) 1.0OMI10S Financials SAP Management of Internal Controls (MIC) 1.0 OMM25S Netweaver - MI SAP Mobile Asset Mgmt. 2.5OMS10S Netweaver - MI SAP Mobile Sales Online 1.0OMT16S Netweaver - MI SAP Mobile Time and Travel 1.6OMU10S Netweaver - MI SAP Mobile Asset ManagementOOG47S Industry SAP Oil and Gas 4.72OPM47S Industry SAP Patient Management 4.72OPOB20 Industry SAP Price Optimisation for BankingOPS04S CRM SAP Prof.Services (Edition 2004)OR3E20 R/3 Enterprise SAP R/3 Enterprise Ext.Set 2.0OR3ENT R/3 Enterprise SAP R/3 Enterprise Ext.Set 1.10ORF04S SCM SAP RFID Enabled Supply ChainORF05S SCM SAP Auto-ID Infrastructure 4.0ORM21S Life-Cycle Data Management SAP Recipe Management 2.1 ORT47S Industry SAP RetailOSC40S SCM SAP SCM 4.0OSC41S SCM SAP SCM 4.1OSC50S SCM SAP SCM 5.0OSE32S SEM SAP SEM 3.2OSE35S SEM SAP SEM 3.5OSR20S Netweaver - Web AS SAP WebAS 6.40OSR30S SRM SAP SRM 3.0OSR40S SRM SAP SRM 4.0OSR50S SRM SAP SRM 5.0OTD07 Industry SAP Trade Delivery 2007OTE47S SRM SAP SAP Telecommunications 4.72OTPM1S Trade Promotions SAP TPMOUC10S Industry SAP Utility Customer E-Services 1.0OUT47S Industry SAP Utilities 4.72OWD10S Industry SAP Workforce Deployment 1.0OWS62S Netweaver - Web AS SAP Web AS 6.20OWS63S Netweaver - Web AS SAP Web AS 6.30OWS64S Netweaver - Web AS SAP Web AS 6.40OXE10S Netweaver - Web AS SAP xApp Emissions Management 1.0OXI20S Netweaver - Web AS SAP XI 2.0OXI30S Netweaver - Web AS SAP XI 3.0OXL10S Netweaver - Web AS SAP xApp Integration Exploration and Production (xIEP) 1.0OXP10S Netweaver - Web AS SAP xApp Product Definition 1.0OXQ10S Netweaver - Web AS SAP xApp Cost and QuotationOXR20S Netweaver - Web AS SAP xApp Res.& Program Mgmt. 2.0PLM100 Life-Cycle Data Management Business Processes in Product Life-Cycle Data Management (includes e-learning courses SAP125 and SAP130 )PLM114 Life-Cycle Data Management Basic Data for Manufacturing and Product ManagementPLM115 Life-Cycle Data Management Basic Data for Process ManufacturingPLM120 Life-Cycle Data Management Document Management SystemPLM130 Life-Cycle Data Management ClassificationPLM145 Life-Cycle Data Management Variant Configuration: Modelling and IntegrationPLM146 Life-Cycle Data Management Variant Configuration: Additional Functions and Scenarios PLM150 Life-Cycle Data Management Change & Configuration ManagementPLM160 Life-Cycle Data Management Recipe ManagementPLM200 Project Portfolio Management Business Processes in Project Management (includes e-lea rning courses SAP125 and SAP130 )PLM210 Project Portfolio Management Project Management - StructuresPLM220 Project Portfolio Management Project Management – LogisticsPLM230 Project Portfolio Management Project Management – ControllingPLM240 Project Portfolio Management Project Management - ReportingPLM280 Project Portfolio Management Resource Related BillingPLM281 Project Portfolio Management HR - Workforce Planning IntegrationPLM300 PLM Business Processes in Plant Maintenance (includes e-learning courses SAP125 and S AP130 )PLM301 PLM Customer ServicePLM305 PLM Managing Technical ObjectsPLM310 PLM Maintenance and Service Processing: PreventativePLM315 PLM Maintenance Processing: Operational FunctionsPLM316 PLM Maintenance Processing: Controlling and Reporting FunctionsPLM318 PLM Analytics in Enterprise Asset ManagementPLM320 PLM WCM Work Clearance ManagementPLM322 PLM Capacity Planning & Time Scheduling in PM ProjectsPLM330 PLM Service ContractsPLM335 PLM Service ProcessingPLM400 PLM Business Processes in Quality Management (includes e-learning courses SAP125 and SAP130)PLM412 PLM Quality Planning and InspectionPLM415 PLM Quality Management in LogisticsPLM420 PLM Quality Management in Discrete ManufacturingPLM421 PLM Quality Management in the Process Industry。
人工智能考试题及答案一、单选题（每题2分，共40分）1. 人工智能的英文缩写是（）。
A. AIB. MLC. DLD. NLP答案：A2. 人工智能之父是（）。
A. 艾伦·图灵B. 约翰·麦卡锡C. 马文·闵斯基D. 艾伦·纽厄尔答案：B3. 以下哪个算法不属于监督学习算法（）。
A. 决策树B. 支持向量机C. 随机森林D. 聚类答案：D4. 在深度学习中，卷积神经网络（CNN）主要用于处理（）。
A. 文本数据B. 图像数据C. 音频数据D. 时间序列数据答案：B5. 以下哪个是强化学习中的基本概念（）。
A. 特征B. 标签C. 奖励D. 损失答案：C6. 以下哪个不是自然语言处理（NLP）的任务（）。
A. 机器翻译B. 文本分类C. 语音识别D. 图像识别答案：D7. 在人工智能中，过拟合是指（）。
A. 模型在训练集上表现很好，在新数据上表现也很好B. 模型在训练集上表现很好，在新数据上表现差C. 模型在训练集上表现差，在新数据上表现好D. 模型在训练集和新数据上表现都差答案：B8. 以下哪个算法是无监督学习算法（）。
A. 逻辑回归B. 线性回归C. K-means聚类D. 神经网络答案：C9. 以下哪个是人工智能中的伦理问题（）。
A. 数据隐私B. 模型泛化C. 特征选择D. 算法优化答案：A10. 以下哪个是人工智能中的可解释性问题（）。
A. 模型训练B. 模型评估C. 模型解释D. 模型部署答案：C11. 以下哪个是人工智能中的偏见问题（）。
A. 数据不平衡B. 模型过拟合C. 数据偏见D. 算法复杂度答案：C12. 以下哪个是人工智能中的鲁棒性问题（）。
A. 模型泛化B. 模型过拟合C. 模型解释D. 算法优化答案：A13. 以下哪个是人工智能中的公平性问题（）。
A. 数据隐私B. 模型泛化C. 算法公平D. 算法优化答案：C14. 以下哪个是人工智能中的透明度问题（）。
