人工智能与知识工程【英文】
课文翻译 整理版222
Unit 1Artificial intelligence，computer programs. 人工智能是制造智能机器的科学与工程，特别是智能化的计算机程序。
It is related，observable.这与使用计算机来理解人类智能的类似任务有关，但是人工智能不需要把它局限在生物可观察的方法上。
In this unit,research .在这个单元，两个章节提出了人工智能研究的概况。
Text A briefly，so on.文章A简要介绍了人工智能的定义，人工智能的系统的几种体系结构、基本功能以及程序等等。
Text B Turing’s Test.文章B解释特定地区研究人工智能的自然语言处理包括定义和传说中的图灵测试。
Unit 2Telecommunication , social advancement.电信网络已成为战略组成的全球基础设施来支持经济的发展，科学发现，教育机会和社会进步。
They are rapidly, wireless facilities.他们迅速发展为支持集成的多媒体服务。
包括语言，数据，在基于光纤的有线和无线设备，和基于蜂窝的全运动视频图像。
Text A provides ，and services.文章A提供了一个全面的内容概述全球移动通信系统。
包括GSM，基本概念的规范，网络和服务。
Text B introduces，cell phone.文章B介绍了现代电信的一种新发明，即多功能手机。
Some topics ，in detail.关于它的基本制造过程中的一些主题，目标受众和潜在的分布都进行了详细的讨论。
Unit 3Internet seems ，in this unit . 互联网似乎已成为现代生活不可或缺的一部分。
但它既有优点也有缺点，可能在这一单元的第二段中看到。
Text A points ，who wants it . 文章指出，人们可以通过互联网获取大量个人信息，然后告诉我们想要的信息。
人工智能英文课件
Supervised learning is a type of machine learning where the algorithm is provided with labeled training data The goal is to learn a function that maps input data to desired outputs based on the provided labels Common examples include classification and regression tasks
Deep learning is a type of machine learning that uses neural networks with multiple layers of hidden units to learn complex patterns and representations from data It is based on biomimetic neural networks and self-organizing mapping networks.
Machine translation is the process of automatically translating text or speech from one language to another using computer algorithms and language data banks This technology has identified the need for human translators in many scenarios
Some challenges associated with deep learning include the requirement for large amounts of labeled data, the complexity of explaining the learned patterns or representations, and the potential for overflow or poor generalization to unseen data
人工智能 专业英语
Develo pment
Achiev ement
Applic ations
2016/5/18
Present
Neural Networks 1986
Knowledge Engineering 1977
Difficulties 1966
Birth of AI 1956 Turing Test 1936
SumAchiev ement
Applic ations
2016/5/18
• The main areas • General machine Natural Computer intelligence, conversationl language vision learning behavior ,data-mining, AI • Driverless cars, robot Pattern Expert recognition soccer and games system
2016/5/18
Devel opme nt
Achiev ement
What is AI? Applica tion
2016/5/18
2016/5/18
Artificial Intelligence (AI) is the
intelligence of machines and the
branch of computer science that aims to create it. Definition in AI textbook :”the study and design of intelligent agents”
Summa ry
Develo pment
Achiev ement
大学各专业名称英文翻译(一)——工学_ENGINEERING
大学各专业名称英文翻译（一）——工学ENGINEERING课程中文名称课程英文名称高等数理方法Advanced Mathematical Method弹塑性力学Elastic-Plastic Mechanics板壳理论Theory of Plate and Shell高等工程力学Advanced Engineering Mechanics板壳非线性力学Nonlinear Mechanics of Plate and Shell复合材料结构力学Structural Mechanics of Composite Material弹性元件的理论及设计Theory and Design of Elastic Element非线性振动Nonlinear Vibration高等土力学Advanced Soil Mechanics分析力学Analytic Mechanics随机振动Random Vibration数值分析Numerical Analysis基础工程计算与分析Calculation and Analysis of Founda tion Engineering结构动力学Structural Dynamics实验力学Laboratory Mechanics损伤与断裂Damage and Fracture小波分析Wavelet Analysis有限元与边界元分析方法Analytical Method of Finite Element and Boundary Element最优化设计方法Optimal Design Method弹性力学Elastic Mechanics高层建筑基础Tall Building Foundation动力学Dynanics土的本构关系Soil Constitutive Relation数学建模Mathematical Modeling现代通信理论与技术Emerging Communications Theory and Technology数字信号处理Digital Signal Processing网络理论与多媒体技术Multi-media and Network Technology医用电子学Electronics for Medicine计算微电子学Computational Microelectronics集成电路材料和系统电子学Material and System Electronics for In tegrated Circuits网络集成与大型数据库Computer Network Integrating Technology and Large scale Database 现代数字系统Modern Digital System微机应用系统设计Microcomputer Application Design计算机网络新技术Modern Computer Network Technologies网络信息系统Network Information System图像传输与处理Image Transmission and Processing图像编码理论Theory of Image Coding遥感技术Remote Sensing Techniques虚拟仪器系统设计Design of Virtual Instrument System生物医学信号处理技术Signal Processing for Biology and Medicine光纤光学Fiber OpticsVLSI的EDA技术EDA Techniques for VLSI电子系统的ASIC技术ASIC Design TechnologiesVLSI技术与检测方法VLSI Techniques & Its Examination专题阅读或专题研究The Special Subject Study信息论Information Theory半导体物理学Semiconductor Physics通信原理Principle of Communication现代数理逻辑Modern Mathematical Logic算法分析与设计Analysis and Design of Algorithms高级计算机网络Advanced Computer Networks高级软件工程Advanced Software Engineering数字图像处理Digital Image Processing知识工程原理Principles of Knowledge Engineering面向对象程序设计Object-Oriented Programming形式语言与自动机Formal Languages and Automata人工智能程序设计Artificial Intelligence Programming软件质量与测试Software Quality and Testing大型数据库原理与高级开发技术Principles of Large-Scale Data-Bas e and Advanced Development Technology自然智能与人工智能Natural Intelligence and Artificial Intelligence Unix操作系统分析Analysis of Unix System计算机图形学Computer GraphicsInternet与Intranet技术Internet and Intranet Technology多媒体技术Multimedia Technology数据仓库技术与联机分析处理Data Warehouse and OLAP程序设计方法学Methodology of Programming计算机信息保密与安全Secrecy and Security of Computer Information电子商务Electronic Commerce分布式系统与分布式处理Distributed Systems and Distributed Processing并行处理与并行程序设计Parallel Processing and Parallel Programming模糊信息处理技术Fuzzy Information Processing Technology人工神经网络及应用Artificial Intelligence and Its Applications Unix编程环境Unix Programming Environment计算机视觉Computer Vision高级管理信息系统Advanced Management Information Systems信息系统综合集成理论及方法Theory and Methodology of Information n System Integration计算机科学研究新进展Advances in Computer Science离散数学Discrete Mathematics操作系统Operating System数据库原理Principles of Database编译原理Principles of Compiler程序设计语言Programming Language数据结构Data Structure计算机科学中的逻辑学Logic in Computer Science面向对象系统分析与设计Object-Oriented System Analysis and Design高等数值分析Advanced Numeric Analysis人工智能技术Artificial Intelligence Technology软计算理论及应用Theory and Application of Soft-Computing逻辑程序设计与专家系统Logic Programming and Expert Systems模式识别Pattern Recognition软件测试技术Software Testing Technology高级计算机网络与集成技术Advanced Computer Networks and Integration Technology 语音信号处理Speech Signal Processing系统分析与软件工具System Analysis and Software Tools计算机仿真Computer Simulation计算机控制Computer Control图像通信技术Image Communication Technology人工神经网络及应用Artificial Intelligence and Its Applications计算机技术研究新进展Advances in Computer Technology环境生物学Environmental Biology水环境生态学模型Models of Water Quality环境化学Environmental Chemistry环境生物技术Environmental Biotechnology水域生态学Aquatic Ecology环境工程Environmental Engineering环境科学研究方法Study Methodology of Environmental Science藻类生理生态学Ecological Physiology in Algae水生动物生理生态学Physiological Ecology of Aquatic Animal专业文献综述Review on Special Information废水处理与回用Sewage Disposal and Re-use生物医学材料学及实验Biomaterials and Experiments现代测试分析Modern Testing Technology and Methods生物材料结构与性能Structures and Properties of Biomaterials计算机基础Computer Basis医学信息学Medical Informatics计算机汇编语言Computer Assembly Language学科前沿讲座Lectures on Frontiers of the Discipline组织工程学Tissue Engineering生物医学工程概论Introduction to Biomedical Engineering高等生物化学Advanced Biochemistry光学与统计物理Optics and Statistical Physics图像分析Image Treatment数据处理分析与建模Data Analysis and Constituting Model高级数据库Advanced Database计算机网络Computer Network多媒体技术Technology of Multimedia软件工程Software Engineering药物化学Pharmaceutical Chemistry功能高分子Functional Polymer InternetIntranet程序设计方法学Methods of Programming InternetIntranet高分子化学与物理Polymeric Chemistry and Physics医学电子学Medical Electronics现代仪器分析Modern Instrumental Analysis仪器分析实验Instrumental Analysis Experiment食品添加剂Food Additives Technology高级食品化学Advanced Food Chemistry食品酶学Food Enzymology现代科学前沿选论Literature on Advances of Modern Science波谱学Spectroscopy波谱学实验Spectroscopic Experiment食品贮运与包装Food Packaging液晶化学Liquid Crystal Chemistry高等有机化学Advanced organic Chemistry功能性食品Function Foods食品营养与卫生学Food Nutrition and Hygiene食品生物技术Food Biotechnology食品研究与开发Food Research and Development有机合成化学Synthetic organic Chemistry食品分离技术Food Separation Technique精细化工装备Refinery Chemical Equipment食品包装原理Principle of Food Packaging表面活性剂化学及应用Chemistry and Application of Surfactant天然产物研究与开发Research and Development of Natural Products 食品工艺学Food Technology生物化学Biochemistry食品分析Food Analysis食品机械与设备Food Machinery and Equipment。
智能科学与技术专业英语
智能科学与技术专业英语一、单词1. Artificial Intelligence (AI)- 英语释义：The theory and development ofputer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision - making, and translation between languages.- 用法：“Artificial Intelligence” is often abbreviated as “AI” and can be used as a subject or in phrases like “AI technology” or “the field of AI”.- 双语例句：- Artificial Intelligence has made great progress in recent years. (近年来，人工智能取得了巨大的进展。
)- Manypanies are investing heavily in artificial intelligence research. (许多公司正在大力投资人工智能研究。
)2. Algorithm- 英语释义：A set ofputational steps and rules for performing a specific task.- 用法：Can be used as a countable noun, e.g. “T his algorithm is very efficient.”- 双语例句：- The new algorithm can solve the problem much faster. (新算法可以更快地解决这个问题。
人工智能英语作文必备知识点
人工智能英语作文必备知识点Title: The Evolution and Impact of Artificial Intelligence.Artificial intelligence (AI) has emerged as a pivotal technology in our modern world, revolutionizing the way we live, work, and interact. Its reach is vast and ever-expanding, touching every aspect of human life from healthcare to entertainment, transportation to education. In this essay, we delve into the evolution of AI, its current applications, and the potential impact it holds for the future.Evolution of AI.The journey of AI began in the early days of computing, when machines were programmed to perform specific tasks. This era was marked by the development of logic programs and expert systems that模仿 human expertise in narrow domains. However, it was the advent of machine learning inthe 1980s and 1990s that truly marked a turning point. Machine learning enabled computers to learn from data and make predictions without being explicitly programmed. This approach revolutionized AI, leading to the emergence of systems that could adapt and improve over time.The past decade has seen a further leap in AI technology with the advent of deep learning. Deep learning algorithms, powered by vast amounts of data and powerful computing resources, have enabled machines to achieve human-like performance in tasks such as image recognition, speech recognition, and natural language processing. This has led to the creation of intelligent assistants, autonomous vehicles, and a range of other cutting-edge applications.Current Applications of AI.AI is now pervasive in our daily lives, shaping the way we interact with technology and the world at large. Here are some of the key areas where AI is making significant impacts:1. Healthcare: AI is revolutionizing healthcare by enabling more accurate diagnoses, personalized treatments, and efficient patient management. Machine learning algorithms can analyze vast amounts of medical data to identify patterns and predict outcomes, assisting doctorsin making informed decisions. AI-powered robots are also being used in surgical procedures, improving precision and reducing human error.2. Education: AI is transforming the education sector by personalizing learning experiences and providing adaptive learning paths for students. Intelligent tutoring systems can identify student strengths and weaknesses and provide targeted feedback and resources. AI-based tools are also being used to analyze student performance data, informing teaching methods and curriculum design.3. Transportation: Autonomous vehicles are one of the most exciting applications of AI. By using a combination of sensors, cameras, and machine learning algorithms, autonomous vehicles can navigate roads safely andefficiently, reducing accidents and traffic congestion. AIis also being used in other areas of transportation, suchas air traffic control and logistics management, improving efficiency and reducing waste.4. Entertainment: AI is revolutionizing the entertainment industry by enabling more interactive and personalized experiences. Recommender systems powered by AI algorithms analyze user preferences and behavior to suggest content that matches their interests. AI is also being used in gaming to create more realistic and engaging environments, as well as in music and art creation,enabling artists to explore new styles and techniques.Future Impact of AI.The potential impact of AI on society and the economyis immense. As AI systems become more intelligent and autonomous, they will likely play an increasingly critical role in various sectors, including manufacturing, finance, and even government. This will lead to increased efficiency, productivity, and innovation, but also present newchallenges and ethical considerations.One of the key challenges is the displacement of jobs due to automation. As AI systems become capable of performing tasks that were traditionally done by humans, there will be a need to retrain workers and create new job opportunities. It will be crucial for governments and educational institutions to invest in skills development and lifelong learning programs to prepare the workforce for the future.Another challenge is the ethical implications of AI. As AI systems become more autonomous, there will be increasing concerns about privacy, security, and accountability. It will be essential to develop robust frameworks and regulations to ensure that AI systems are designed and used ethically, respecting human rights and values.Despite these challenges, the potential benefits of AI are vast. AI systems have the potential to solve complex problems that have previously been intractable, such as climate change and global poverty. By harnessing the powerof data and machine learning, we can make informed decisions and create more effective solutions to address these issues.In conclusion, AI is poised to transform our world in profound ways. It has the potential to bring about remarkable improvements in areas such as healthcare, education, transportation, and entertainment. However, we must also be mindful of the challenges and ethical implications that come with this technology. By investing in skills development, ethical frameworks, and innovative policies, we can harness the power of AI to create a better future for all.。
人工智能概论中英文术语对照表
人工智能概论中英文术语对照表动作action专家系统Expert system人工智能语言AI language祖先过滤形策略ancestry-filtered form strategy与节点AND node与或图AND/OR graph与或树AND/OR tree回答语句answer statement人工智能artificial intelligence,AI原子公式atomic formula自动定理证明automatic theorem provingB规则B-rule倒退值backed-up value回溯backtracking盲目搜索，无信息搜索blind search宽度优先搜索breadth-first search子句clause组合爆炸combinatorial explosion冲突解决conflict resolution合取式conjunct合取conjunction合取范式conjunctive normal form连词，连接词connective一致解图consistant solution graph控制策略control strategy费用cost演绎deduction深度优先搜索depth-first search推导表，引导图derivation graph差别difference有向图directed graph析取式disjunct析取disjunction谓词演算辖域domain in predicate calculus论域，文字域domain of discourse搜索算法的效率efficiency of search algorithm空子句empty clause等价equivalence估计费用estimated cost估值函数evaluation function存在量词existential quantifier扩展节点expending node节点的扩展expansion of nodeF规则F-rule事实fact一阶谓词演算first order predicate calculus 博弈game图graph图表示法graph notation图搜索graph search图搜索控制策略graph-search control strategy 启发函数heuristic function启发信息heuristic information启发搜索heuristic search蕴涵，蕴涵式implication推理inference智能intelligence解释器interpreter知识knowledge知识获取knowledge acquisition全局数据库Global database知识库knowledge base知识工程knowledge engineering学习learning启发式搜索Heuristic search线形输入形策略linear-input form strategy文字literal逻辑logic逻辑连词logic connective逻辑推理logic reasoning匹配match模式匹配match pattern母式matrix最一般合一者most general unifierNP完全问题NP-complete problem算符、算子、操作符operator最优解树optimal solution tree有序搜索ordered search谓词predicate谓词演算predicate calculus谓词逻辑predicate logic前缀prefix本原问题primitive problem问题归约problem-reduction问题求解problem solving产生式production产生式规则production rule量词quantifier推理reasoning正向推理forward reasoning逆向推理backward reasoning推理机reasoning machine归约reduction反演refutation反演树refutation tree归结resolution归结原理resolution principle归结反演resolution refutation归结式resolvent可满足性satisfiability模式识别Pattern recognition量词辖域scope of quantifier搜索search, searching搜索算法searching algorithm搜索图searching graph搜索策略searching strategy搜索树searching tree句子sentence解图solution graph解树solution tree可解节点solvable node可解标示过程solvable labeling procedure 状态state状态空间state space代换例substitution instance代换substitution重言式tautology项term定理证明theorem-proving不确定性uncertainty合一unifier最一般合一most general unifier全称量词universal quantifier不可满足集unsatisfiable set不可解标示过程unsolvable-labeling procedure 不可解节点unsolvable node永真式validity合适公式、合式公式well-formed formula (wff)谓词演算公式wffs of predicate calculus人工神经网络artificial neural network遗传算法genetic algorithm机器学习machine learning。
人工智能英语 Unit 2 Machine Learning
Lead-in
Part I
Part I
Task 1 The following are common terms used in machine learning. Please match them with their Chinese translation. Look them up in a dictionary if necessary.
Part II
Types of machine learning Depending upon the nature of the data and the desired outcome, machine learning are divided into 4 primary types. Supervised machine learning Addressing datasets with labels or structure，data acts as a teacher and “trains” the machine, increasing in its ability to make a prediction or decision.
Part I
Task 2 Listen to the short passage and choose the proper words to fill in the blanks.
In the past, humans built algorithmic bots by giving them instructions that humans could 1________. If this, than that. But many problems are just too big and complex for a human to write simple instructions for. There are countless videos on Tiktok, which ones should the users see as 2______________? There’s a a huge amount of financial transactions a second, which ones are fraudulent? For this beautiful dress, what is the 3 ________ price this user will pay right now?
《人工智能》课程教学指南.
《人工智能》课程教学指南.
《人工智能》课程教学指南
课程编号：
英文名称：Artificial Intelligence 周讲课时数：34 学分数：2 课程简介：
人工智能是计算机科学的一个分支，是研究计算机实现智能的原理以及建造智能计算机的科学，人工智能的研究将拓展计算机更深层次的应用。
本课程介绍人工智能的基本原理和一般理论，学习和研究知识表示、逻辑推理和问题求解、自然语言理解等内容。
课程教学目的和要求：
本课程的目的是使计算机专业的学生在掌握了相关的计算机基本理论的基础上，对人工智能的基本概念和原理有一个较为全面的了解，掌握现代流行的智能处理的主要技术和方法，为智能信息分析和构建专家系统、智能决策支持系统等各类智能系统奠定基础。
教材：
1、《人工智能原理》石纯一
清华大学出版社
参考书：
1、《人工智能原理及其应用》周西苓
南京航空航天大学出版社
2、《人工智能与知识工程》陈世福
南京大学出版社
成绩考核方式及评分标准：理论与技能综合考查（期末）。
主讲教师：张亮1。
人工智能英文版
The first 10 years of achievement
In terms of problem solving(问题求解): 1960 Newell(纽厄尔) compiled a universal problem solver (GPS), can solve 11 different types of problems; In expert systems(专家系统): the results of the 1968 Feigenbaum(费根鲍姆) developed DENDRAL expert system and put into use;
Systems that think rationally
“The study of mental faculties through the use of computational models” (Charniak et al. 1985)
Systems that act like humans
Secondary Translation - Machine Translation the spirit is willing but the flesh is weak the vodka is good but the meat is rotten Combinatorial explosion problem (组合爆炸) The fact that a program can find a solution in principle does not means that the program contains any of the mechanisms needed to find it in practice. Perceptron limitations(感知机局限性) A two-input perceptron cannot be trained to recognize when its two inputs are different.
计算机专业英语Unit 15 Artificial Intelligence
AI Techniques
❖ Expert systems: computer application that makes decisions in real-life situations that would otherwise be performed by a human expert.
features can be added to computers to make them more useful tools.
Computer English
AI Category-2
❖AI has two aspects: “Statistical〞 and “Classical AI 〞.
Computer English
Loebner Prize
❖Loebner Prize, a scaled down version of the Turing test, requires that machines have to "converse" with testers only on a limited topic.
Computer English
AI Category-1
❖ AI is divide AI into two classes: strong AI and weak AI. ❖ strong AI: makes the bold claim that computers can be made
to think on a level (at least) equal to humans. ❖ weak AI: Weak AI simply states that some "thinking-like"
人工智能专业英语
人工智能专业英语Artificial Intelligence has become one of the most popular trends in the contemporary world. With its applications close at hand, AI has given rise to a huge revolution in the course of our lives.AI is a specialized field of computer science research that strives to have computers think and respond to human behavior in increasingly naturalistic and intelligent ways. It is an interdisciplinary branch of study that combines the application of algorithms and methods from mathematics, engineering, and computer science, as well as information sciences, cognitive science, and other disciplines to enable machines to engage in the most complex human tasks such as driving a car or diagnosing a disease.AI is responsible for all the applications that can interpret human behavior and decision making, such as text analysis, image and video recognition, natural language processing, robotics, autonomous systems and machine learning. This technology can interpret physical and mental activities, interpret, respond and decide based on the data provided. It allows machines to translate accurately language and can recognize a person's voice and understand the meaning of the words being said.AI also assists us in making more efficient decisions. The automation of decision-making processes, thanks to artificial intelligence, is becoming increasingly common. AI is also supporting the management of resources, financial analysis and forecasting, risk analysis and other operations.AI has the power to revolutionize any industry and the already existing applications have created a lot of incremental value. Companies are recognizing the relevance of AI in driving growth and increasing market share. AI promises increased efficiency, better outcomes, faster delivery, and new ways of doing business, making it an essential part of modern life.。
人工智能专业词汇
Letter AAccumulated error backpropagation累积误差逆传播Activation Function激活函数Adaptive Resonance Theory/ART自适应谐振理论Addictive model加性学习Adversarial Networks对抗网络Affine Layer仿射层Affinity matrix亲和矩阵Agent代理/ 智能体Algorithm算法Alpha-beta pruningα-β剪枝Anomaly detection异常检测Approximation近似Area Under ROC Curve／AUC R oc 曲线下面积Artificial General Intelligence/AGI通用人工智能Artificial Intelligence/AI人工智能Association analysis关联分析Attention mechanism注意力机制Attribute conditional independence assumption属性条件独立性假设Attribute space属性空间Attribute value属性值Autoencoder自编码器Automatic speech recognition自动语音识别Automatic summarization自动摘要Average gradient平均梯度Average-Pooling平均池化Letter BBackpropagation Through Time通过时间的反向传播Backpropagation/BP反向传播Base learner基学习器Base learning algorithm基学习算法Batch Normalization/BN批量归一化Bayes decision rule贝叶斯判定准则Bayes Model Averaging／BMA贝叶斯模型平均Bayes optimal classifier贝叶斯最优分类器Bayesian decision theory贝叶斯决策论Bayesian network贝叶斯网络Between-class scatter matrix类间散度矩阵Bias偏置/ 偏差Bias-variance decomposition偏差-方差分解Bias-Variance Dilemma偏差–方差困境Bi-directional Long-Short Term Memory/Bi-LSTM双向长短期记忆Binary classification二分类Binomial test二项检验Bi-partition二分法Boltzmann machine玻尔兹曼机Bootstrap sampling自助采样法／可重复采样／有放回采样Bootstrapping自助法Break-Event Point／BEP平衡点Letter CCalibration校准Cascade-Correlation级联相关Categorical attribute离散属性Class-conditional probability类条件概率Classification and regression tree/CART分类与回归树Classifier分类器Class-imbalance类别不平衡Closed -form闭式Cluster簇/类/集群Cluster analysis聚类分析Clustering聚类Clustering ensemble聚类集成Co-adapting共适应Coding matrix编码矩阵COLT国际学习理论会议Committee-based learning基于委员会的学习Competitive learning竞争型学习Component learner组件学习器Comprehensibility可解释性Computation Cost计算成本Computational Linguistics计算语言学Computer vision计算机视觉Concept drift概念漂移Concept Learning System /CLS概念学习系统Conditional entropy条件熵Conditional mutual information条件互信息Conditional Probability Table／CPT条件概率表Conditional random field/CRF条件随机场Conditional risk条件风险Confidence置信度Confusion matrix混淆矩阵Connection weight连接权Connectionism连结主义Consistency一致性／相合性Contingency table列联表Continuous attribute连续属性Convergence收敛Conversational agent会话智能体Convex quadratic programming凸二次规划Convexity凸性Convolutional neural network/CNN卷积神经网络Co-occurrence同现Correlation coefficient相关系数Cosine similarity余弦相似度Cost curve成本曲线Cost Function成本函数Cost matrix成本矩阵Cost-sensitive成本敏感Cross entropy交叉熵Cross validation交叉验证Crowdsourcing众包Curse of dimensionality维数灾难Cut point截断点Cutting plane algorithm割平面法Letter DData mining数据挖掘Data set数据集Decision Boundary决策边界Decision stump决策树桩Decision tree决策树／判定树Deduction演绎Deep Belief Network深度信念网络Deep Convolutional Generative Adversarial Network/DCGAN深度卷积生成对抗网络Deep learning深度学习Deep neural network/DNN深度神经网络Deep Q-Learning深度Q 学习Deep Q-Network深度Q 网络Density estimation密度估计Density-based clustering密度聚类Differentiable neural computer可微分神经计算机Dimensionality reduction algorithm降维算法Directed edge有向边Disagreement measure不合度量Discriminative model判别模型Discriminator判别器Distance measure距离度量Distance metric learning距离度量学习Distribution分布Divergence散度Diversity measure多样性度量／差异性度量Domain adaption领域自适应Downsampling下采样D-separation （Directed separation）有向分离Dual problem对偶问题Dummy node哑结点Dynamic Fusion动态融合Dynamic programming动态规划Letter EEigenvalue decomposition特征值分解Embedding嵌入Emotional analysis情绪分析Empirical conditional entropy经验条件熵Empirical entropy经验熵Empirical error经验误差Empirical risk经验风险End-to-End端到端Energy-based model基于能量的模型Ensemble learning集成学习Ensemble pruning集成修剪Error Correcting Output Codes／ECOC纠错输出码Error rate错误率Error-ambiguity decomposition误差-分歧分解Euclidean distance欧氏距离Evolutionary computation演化计算Expectation-Maximization期望最大化Expected loss期望损失Exploding Gradient Problem梯度爆炸问题Exponential loss function指数损失函数Extreme Learning Machine/ELM超限学习机Letter FFactorization因子分解False negative假负类False positive假正类False Positive Rate/FPR假正例率Feature engineering特征工程Feature selection特征选择Feature vector特征向量Featured Learning特征学习Feedforward Neural Networks/FNN前馈神经网络Fine-tuning微调Flipping output翻转法Fluctuation震荡Forward stagewise algorithm前向分步算法Frequentist频率主义学派Full-rank matrix满秩矩阵Functional neuron功能神经元Letter GGain ratio增益率Game theory博弈论Gaussian kernel function高斯核函数Gaussian Mixture Model高斯混合模型General Problem Solving通用问题求解Generalization泛化Generalization error泛化误差Generalization error bound泛化误差上界Generalized Lagrange function广义拉格朗日函数Generalized linear model广义线性模型Generalized Rayleigh quotient广义瑞利商Generative Adversarial Networks/GAN生成对抗网络Generative Model生成模型Generator生成器Genetic Algorithm/GA遗传算法Gibbs sampling吉布斯采样Gini index基尼指数Global minimum全局最小Global Optimization全局优化Gradient boosting梯度提升Gradient Descent梯度下降Graph theory图论Ground-truth真相／真实Letter HHard margin硬间隔Hard voting硬投票Harmonic mean调和平均Hesse matrix海塞矩阵Hidden dynamic model隐动态模型Hidden layer隐藏层Hidden Markov Model/HMM隐马尔可夫模型Hierarchical clustering层次聚类Hilbert space希尔伯特空间Hinge loss function合页损失函数Hold-out留出法Homogeneous同质Hybrid computing混合计算Hyperparameter超参数Hypothesis假设Hypothesis test假设验证Letter IICML国际机器学习会议Improved iterative scaling/IIS改进的迭代尺度法Incremental learning增量学习Independent and identically distributed/i.i.d.独立同分布Independent Component Analysis/ICA独立成分分析Indicator function指示函数Individual learner个体学习器Induction归纳Inductive bias归纳偏好Inductive learning归纳学习Inductive Logic Programming／ILP归纳逻辑程序设计Information entropy信息熵Information gain信息增益Input layer输入层Insensitive loss不敏感损失Inter-cluster similarity簇间相似度International Conference for Machine Learning/ICML国际机器学习大会Intra-cluster similarity簇内相似度Intrinsic value固有值Isometric Mapping/Isomap等度量映射Isotonic regression等分回归Iterative Dichotomiser迭代二分器Letter KKernel method核方法Kernel trick核技巧Kernelized Linear Discriminant Analysis／KLDA核线性判别分析K-fold cross validation k 折交叉验证／k 倍交叉验证K-Means Clustering K –均值聚类K-Nearest Neighbours Algorithm/KNN K近邻算法Knowledge base知识库Knowledge Representation知识表征Letter LLabel space标记空间Lagrange duality拉格朗日对偶性Lagrange multiplier拉格朗日乘子Laplace smoothing拉普拉斯平滑Laplacian correction拉普拉斯修正Latent Dirichlet Allocation隐狄利克雷分布Latent semantic analysis潜在语义分析Latent variable隐变量Lazy learning懒惰学习Learner学习器Learning by analogy类比学习Learning rate学习率Learning Vector Quantization/LVQ学习向量量化Least squares regression tree最小二乘回归树Leave-One-Out/LOO留一法linear chain conditional random field线性链条件随机场Linear Discriminant Analysis／LDA线性判别分析Linear model线性模型Linear Regression线性回归Link function联系函数Local Markov property局部马尔可夫性Local minimum局部最小Log likelihood对数似然Log odds／logit对数几率Logistic Regression Logistic 回归Log-likelihood对数似然Log-linear regression对数线性回归Long-Short Term Memory/LSTM长短期记忆Loss function损失函数Letter MMachine translation/MT机器翻译Macron-P宏查准率Macron-R宏查全率Majority voting绝对多数投票法Manifold assumption流形假设Manifold learning流形学习Margin theory间隔理论Marginal distribution边际分布Marginal independence边际独立性Marginalization边际化Markov Chain Monte Carlo/MCMC马尔可夫链蒙特卡罗方法Markov Random Field马尔可夫随机场Maximal clique最大团Maximum Likelihood Estimation/MLE极大似然估计／极大似然法Maximum margin最大间隔Maximum weighted spanning tree最大带权生成树Max-Pooling最大池化Mean squared error均方误差Meta-learner元学习器Metric learning度量学习Micro-P微查准率Micro-R微查全率Minimal Description Length/MDL最小描述长度Minimax game极小极大博弈Misclassification cost误分类成本Mixture of experts混合专家Momentum动量Moral graph道德图／端正图Multi-class classification多分类Multi-document summarization多文档摘要Multi-layer feedforward neural networks多层前馈神经网络Multilayer Perceptron/MLP多层感知器Multimodal learning多模态学习Multiple Dimensional Scaling多维缩放Multiple linear regression多元线性回归Multi-response Linear Regression ／MLR多响应线性回归Mutual information互信息Letter NNaive bayes朴素贝叶斯Naive Bayes Classifier朴素贝叶斯分类器Named entity recognition命名实体识别Nash equilibrium纳什均衡Natural language generation/NLG自然语言生成Natural language processing自然语言处理Negative class负类Negative correlation负相关法Negative Log Likelihood负对数似然Neighbourhood Component Analysis/NCA近邻成分分析Neural Machine Translation神经机器翻译Neural Turing Machine神经图灵机Newton method牛顿法NIPS国际神经信息处理系统会议No Free Lunch Theorem／NFL没有免费的午餐定理Noise-contrastive estimation噪音对比估计Nominal attribute列名属性Non-convex optimization非凸优化Nonlinear model非线性模型Non-metric distance非度量距离Non-negative matrix factorization非负矩阵分解Non-ordinal attribute无序属性Non-Saturating Game非饱和博弈Norm范数Normalization归一化Nuclear norm核范数Numerical attribute数值属性Letter OObjective function目标函数Oblique decision tree斜决策树Occam’s razor奥卡姆剃刀Odds几率Off-Policy离策略One shot learning一次性学习One-Dependent Estimator／ODE独依赖估计On-Policy在策略Ordinal attribute有序属性Out-of-bag estimate包外估计Output layer输出层Output smearing输出调制法Overfitting过拟合／过配Oversampling过采样Letter PPaired t-test成对t 检验Pairwise成对型Pairwise Markov property成对马尔可夫性Parameter参数Parameter estimation参数估计Parameter tuning调参Parse tree解析树Particle Swarm Optimization/PSO粒子群优化算法Part-of-speech tagging词性标注Perceptron感知机Performance measure性能度量Plug and Play Generative Network即插即用生成网络Plurality voting相对多数投票法Polarity detection极性检测Polynomial kernel function多项式核函数Pooling池化Positive class正类Positive definite matrix正定矩阵Post-hoc test后续检验Post-pruning后剪枝potential function势函数Precision查准率／准确率Prepruning预剪枝Principal component analysis/PCA主成分分析Principle of multiple explanations多释原则Prior先验Probability Graphical Model概率图模型Proximal Gradient Descent/PGD近端梯度下降Pruning剪枝Pseudo-label伪标记Letter QQuantized Neural Network量子化神经网络Quantum computer量子计算机Quantum Computing量子计算Quasi Newton method拟牛顿法Letter RRadial Basis Function／RBF径向基函数Random Forest Algorithm随机森林算法Random walk随机漫步Recall查全率／召回率Receiver Operating Characteristic/ROC受试者工作特征Rectified Linear Unit/ReLU线性修正单元Recurrent Neural Network循环神经网络Recursive neural network递归神经网络Reference model参考模型Regression回归Regularization正则化Reinforcement learning/RL强化学习Representation learning表征学习Representer theorem表示定理reproducing kernel Hilbert space/RKHS再生核希尔伯特空间Re-sampling重采样法Rescaling再缩放Residual Mapping残差映射Residual Network残差网络Restricted Boltzmann Machine/RBM受限玻尔兹曼机Restricted Isometry Property/RIP限定等距性Re-weighting重赋权法Robustness稳健性/鲁棒性Root node根结点Rule Engine规则引擎Rule learning规则学习Letter SSaddle point鞍点Sample space样本空间Sampling采样Score function评分函数Self-Driving自动驾驶Self-Organizing Map／SOM自组织映射Semi-naive Bayes classifiers半朴素贝叶斯分类器Semi-Supervised Learning半监督学习semi-Supervised Support Vector Machine半监督支持向量机Sentiment analysis情感分析Separating hyperplane分离超平面Sigmoid function Sigmoid 函数Similarity measure相似度度量Simulated annealing模拟退火Simultaneous localization and mapping同步定位与地图构建Singular Value Decomposition奇异值分解Slack variables松弛变量Smoothing平滑Soft margin软间隔Soft margin maximization软间隔最大化Soft voting软投票Sparse representation稀疏表征Sparsity稀疏性Specialization特化Spectral Clustering谱聚类Speech Recognition语音识别Splitting variable切分变量Squashing function挤压函数Stability-plasticity dilemma可塑性-稳定性困境Statistical learning统计学习Status feature function状态特征函Stochastic gradient descent随机梯度下降Stratified sampling分层采样Structural risk结构风险Structural risk minimization/SRM结构风险最小化Subspace子空间Supervised learning监督学习／有导师学习support vector expansion支持向量展式Support Vector Machine/SVM支持向量机Surrogat loss替代损失Surrogate function替代函数Symbolic learning符号学习Symbolism符号主义Synset同义词集Letter TT-Distribution Stochastic Neighbour Embedding/t-SNE T –分布随机近邻嵌入Tensor张量Tensor Processing Units/TPU张量处理单元The least square method最小二乘法Threshold阈值Threshold logic unit阈值逻辑单元Threshold-moving阈值移动Time Step时间步骤Tokenization标记化Training error训练误差Training instance训练示例／训练例Transductive learning直推学习Transfer learning迁移学习Treebank树库Tria-by-error试错法True negative真负类True positive真正类True Positive Rate/TPR真正例率Turing Machine图灵机Twice-learning二次学习Letter UUnderfitting欠拟合／欠配Undersampling欠采样Understandability可理解性Unequal cost非均等代价Unit-step function单位阶跃函数Univariate decision tree单变量决策树Unsupervised learning无监督学习／无导师学习Unsupervised layer-wise training无监督逐层训练Upsampling上采样Letter VVanishing Gradient Problem梯度消失问题Variational inference变分推断VC Theory VC维理论Version space版本空间Viterbi algorithm维特比算法Von Neumann architecture冯·诺伊曼架构Letter WWasserstein GAN/WGAN Wasserstein生成对抗网络Weak learner弱学习器Weight权重Weight sharing权共享Weighted voting加权投票法Within-class scatter matrix类内散度矩阵Word embedding词嵌入Word sense disambiguation词义消歧Letter ZZero-data learning零数据学习Zero-shot learning零次学习---------------------作者：业余草来源：CSDN原文：https:///xmtblog/article/details/76537364 版权声明：本文为博主原创文章，转载请附上博文链接！。
学英语artificialintelligence人工智能
学英语artificialintelligence人工智能
With the development of the artificial intelligence and computer vision technology, service-oriented robots have appeared in various fields.
随着人工智能和计算机视觉技术的发展与
成熟，服务型机器人已出现在各个领域。
【核心词汇】artificial intelligence
the branch of computer science that
deal with writing computer programs that
can solve problems creatively
人工智能
【例句拓展】
1. They have made great progress in machine translation and artificial intelligence.
他们在机器翻译和
人工智能
方面取得了重大进展。
2. We should not turn a blind eye to the disadvantages of artificial intelligence.
turn a blind eye to
视而不见，熟视无睹
我们不应该对人工智能的坏处视而不见。
3. This is also the case with agent technology, as it is a form
of artificial intelligence.
这同样适用于代理技术的情况，因为代理技术也是
人工智能
的一种。
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N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Heuristic Problem Solving
Figure 1.1 Heuristics as means of obtaining restricted projections from the domain space D into the solution space S.
N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Heuristic Problem Solving (cont)
Figure 1.2: (a) Ill-informed and (b) well-informed heuristics. They are represented as `patches' in the problem space. The patches have different forms (usually quadrilateral) depending on the way of representing the heuristics in a computer program.
N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Heuristic Problem Solving (cont)
Figure 1.3: The problem knowledge maps the domain space into the solution space and approximates the objective (goal) function: (a) a general case; (b) two dimensional case.
N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Introduction to the AI Paradigms (cont)
AI directions: developing methods and systems for solving AI problems without following the way the humans do (expert systems) developing methods and systems for solving AI problems through modelling the human way of thinking, or the way the brain works (neural networks) AI paradigms: symbolic or sub-symbolic (connectionist)
N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Introduction to the AI Paradigms
AI objectives: to develop methods and systems for solving problems, usually solved through intellectual activity of humans, eg. image recognition language and speech processing; planning, prediction, etc., thus enhancing the computer information systems to improve our understanding on how the human brain works
N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Genetic Algorithms and Evolutionary Programming
An Introduction to Artificial Intelligence and Knowledge Engineering
N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Sub-topics:
Introduction to the AI paradigms (1.1; pp. 1-3) Heuristic problem solving (1.2; pp. 3-9) Genetic algorithms and evolutionary programming (1.2.3; pp. 9-14) Expert systems (1.3.1; pp. 14-15) Fuzzy systems (1.3.2; pp. 15-17) Neural networks (1.3.3; pp. 17-19) Hybrid systems (1.3.4; 1.9, pp. 65-68)