ALBERT

Note: Einleitung --- I Angewandte Mathematik und Grundlagen für das Machine Learning --- Lineare Algebra --- Wahrscheinlichkeits- und Informationstheorie --- Numerische Berechnung --- Grundlagen für das Machine Learning --- II Tiefe Netze: Zeitgemäße Verfahren --- Tiefe Feedforward-Netze --- Regularisierung --- Optimierung beim Trainieren von tiefen Modellen --- CNNs --- Sequenzmodellierung: RNNs und rekursive Netze --- Praxisorientierte Methodologie --- Anwendungen --- III Deep-Learning-Forschung --- Lineare Faktorenmodelle --- Autoencoder --- Representation Learning --- Strukturierte probabilistische Modelle für Deep Learning --- Monte-Carlo-Verfahren --- Die Partitionsfunktion --- Approximative Inferenz --- Tiefe generative Modelle --- Literaturverzeichnis --- Abkürzungsverzeichnis --- Index

Note: Contents I Artificial Intelligence 1 Introduction 1.1 What Is AI? 1.2 The Foundations of Artificial Intelligence 1.3 The History of Artificial Intelligence 1.4 The State of the Art 1.5 Risks and Benefits of AI Summary Bibliographical and Historical Notes 2 Intelligent Agents 2.1 Agents and Environments 2.2 Good Behavior: The Concept of Rationality 2.3 The Nature of Environments 2.4 The Structure of Agents Summary Bibliographical and Historical Notes II Problem-solving 3 Solving Problems by Searching 3.1 Problem-Solving Agents 3.2 Example Problems 3.3 Search Algorithms 3.4 Uninformed Search Strategies 3.5 Informed (Heuristic) Search Strategies 3.6 Heuristic Functions Summary Bibliographical and Historical Notes 4 Search in Complex Environments 4.1 Local Search and Optimization Problems 4.2 Local Search in Continuous Spaces 4.3 Search with Nondeterministic Actions 4.4 Search in Partially Observable Environments 4.5 Online Search Agents and Unknown Environments Summary Bibliographical and Historical Notes 5 Constraint Satisfaction Problems 5.1 Defining Constraint Satisfaction Problems 5.2 Constraint Propagation: Inference in CSPs 5.3 Backtracking Search for CSPs 5.4 Local Search for CSPs 5.5 The Structure of Problems Summary Bibliographical and Historical Notes 6 Adversarial Search and Games 6.1 Game Theory 6.2 Optimal Decisions in Games 6.3 Heuristic Alpha-Beta Tree Search 6.4 Monte Carlo Tree Search 6.5 Stochastic Games 6.6 Partially Observable Games 6.7 Limitations of Game Search Algorithms Summary Bibliographical and Historical Notes III Knowledge, reasoning, and planning 7 Logical Agents 7.1 Knowledge-Based Agents 7.2 The Wumpus World 7.3 Logic 7.4 Propositional Logic: A Very Simple Logic 7.5 Propositional Theorem Proving 7.6 Effective Propositional Model Checking 7.7 Agents Based on Propositional Logic Summary Bibliographical and Historical Notes 8 First-Order Logic 8.1 Representation Revisited 8.2 Syntax and Semantics of First-Order Logic 8.3 Using First-Order Logic 8.4 Knowledge Engineering in First-Order Logic Summary Bibliographical and Historical Notes 9 Inference in First-Order Logic 9.1 Propositional vs. First-Order Inference 9.2 Unification and First-Order Inference 9.3 Forward Chaining 9.4 Backward Chaining 9.5 Resolution Summary Bibliographical and Historical Notes 10 Knowledge Representation 10.1 Ontological Engineering 10.2 Categories and Objects 10.3 Events 10.4 Mental Objects and Modal Logic 10.5 Reasoning Systems for Categories 10.6 Reasoning with Default Information Summary Bibliographical and Historical Notes 11 Automated Planning 11.1 Definition of Classical Planning 11.2 Algorithms for Classical Planning 11.3 Heuristics for Planning 11.4 Hierarchical Planning 11.5 Planning and Acting in Nondeterministic Domains 11.6 Time, Schedules, and Resources 11.7 Analysis of Planning Approaches Summary Bibliographical and Historical Notes IV Uncertain knowledge and reasoning 12 Quantifying Uncertainty 12.1 Acting under Uncertainty 12.2 Basic Probability Notation 12.3 Inference Using Full Joint Distributions 12.4 Independence 12.5 Bayes' Rule and Its Use 12.6 Naive Bayes Models 12.7 The Wumpus World Revisited Summary Bibliographical and Historical Notes 13 Probabilistic Reasoning 13.1 Representing Knowledge in an Uncertain Domain 13.2 The Semantics of Bayesian Networks 13.3 Exact Inference in Bayesian Networks 13.4 Approximate Inference for Bayesian Networks 13.5 Causal Networks Summary Bibliographical and Historical Notes 14 Probabilistic Reasoning over Time 14.1 Time and Uncertainty 14.2 Inference in Temporal Models 14.3 Hidden Markov Models 14.4 Kalman Filters 14.5 Dynamic Bayesian Networks Summary Bibliographical and Historical Notes 15 Making Simple Decisions 15.1 Combining Beliefs and Desires under Uncertainty 15.2 The Basis of Utility Theory 15.3 Utility Functions 15.4 Multiattribute Utility Functions 15.5 Decision Networks 15.6 The Value of Information 15.7 Unknown Preferences Summary Bibliographical and Historical Notes 16 Making Complex Decisions 16.1 Sequential Decision Problems 16.2 Algorithms for MDPs 16.3 Bandit Problems 16.4 Partially Observable MDPs 16.5 Algorithms for Solving POMDPs Summary Bibliographical and Historical Notes 17 Multiagent Decision Making 17.1 Properties of Multiagent Environments 17.2 Non-Cooperative Game Theory 17.3 Cooperative Game Theory 17.4 Making Collective Decisions Summary Bibliographical and Historical Notes 18 Probabilistic Programming 18.1 Relational Probability Models 18.2 Open-Universe Probability Models 18.3 Keeping Track of a Complex World 18.4 Programs as Probability Models Summary Bibliographical and Historical Notes V Machine Learning 19 Learning from Examples 19.1 Forms of Learning 19.2 Supervised Learning 19.3 Learning Decision Trees 19.4 Model Selection and Optimization 19.5 The Theory of Learning 19.6 Linear Regression and Classification 19.7 Nonparametric Models 19.8 Ensemble Learning 19.9 Developing Machine Learning Systems Summary Bibliographical and Historical Notes 20 Knowledge in Learning 20.1 A Logical Formulation of Learning 20.2 Knowledge in Learning 20.3 Explanation-Based Learning 20.4 Learning Using Relevance Information 20.5 Inductive Logic Programming Summary Bibliographical and Historical Notes 21 Learning Probabilistic Models 21.1 Statistical Learning 21.2 Learning with Complete Data 21.3 Learning with Hidden Variables: The EM Algorithm Summary Bibliographical and Historical Notes 22 Deep Learning 22.1 Simple Feedforward Networks 22.2 Computation Graphs for Deep Learning 22.3 Convolutional Networks 22.4 Learning Algorithms 22.5 Generalization 22.6 Recurrent Neural Networks 22.7 Unsupervised Learning and Transfer Learning 22.8 Applications Summary Bibliographical and Historical Notes 23 Reinforcement Learning 23.1 Learning from Rewards 23.2 Passive Reinforcement Learning 23.3 Active Reinforcement Learning 23.4 Generalization in Reinforcement Learning 23.5 Policy Search 23.6 Apprenticeship and Inverse Reinforcement Learning 23.7 Applications of Reinforcement Learning Summary Bibliographical and Historical Notes VI Communicating, perceiving, and acting 24 Natural Language Processing 24.1 Language Models 24.2 Grammar 24.3 Parsing 24.4 Augmented Grammars 24.5 Complications of Real Natural Language 24.6 Natural Language Tasks Summary Bibliographical and Historical Notes 25 Deep Learning for Natural Language Processing 25.1 Word Embeddings 25.2 Recurrent Neural Networks for NLP 25.3 Sequence-to-Sequence Models 25.4 The Transformer Architecture 25.5 Pretraining and Transfer Learning 25.6 State of the art Summary Bibliographical and Historical Notes 26 Robotics 26.1 Robots 26.2 Robot Hardware 26.3 What kind of problem is robotics solving? 26.4 Robotic Perception 26.5 Planning and Control 26.6 Planning Uncertain Movements 26.7 Reinforcement Learning in Robotics 26.8 Humans and Robots 26.9 Alternative Robotic Frameworks 26.10 Application Domains Summary Bibliographical and Historical Notes 27 Computer Vision 27.1 Introduction 27.2 Image Formation 27.3 Simple Image Features 27.4 Classifying Images 27.5 Detecting Objects 27.6 The 3D World 27.7 Using Computer Vision Summary Bibliographical and Historical Notes VII Conclusions 28 Philosophy, Ethics, and Safety of AI 28.1 The Limits of AI 28.2 Can Machines Really Think? 28.3 The Ethics of AI Summary Bibliographical and Historical Notes 29 The Future of AI 29.1 AI Components 29.2 AI Architectures A Mathematical Background A.1 Complexity Analysis and 0() Notation A.2 Vectors, Matrices, and Linear Algebra A.3 Probability Distributions Bibliographical and Historical Notes B Notes on Languages and Algorithms B. l Defining Languages with Backus-Naur Form (BNF) B.2 Describing Algorithms with Pseudocode B.3 Online Supplemental Material Bibliography Index