Artificial Intelligent [AI]

Course outline 

Module 1: Introduction to AI

– What Is AI?

– Foundations of Artificial Intelligence

– History of Artificial Intelligence

– State of the Art in AI

– Risks and Benefits of AI

Module 2: Intelligent Agents

– Agents and Environments

– Concept of Rationality

– Nature of Environments

– Structure of Agents

Module 3: Problem-Solving and Search

– Problem-Solving Agents

– Example Problems

– Search Algorithms

  – Uninformed (Blind) Search Strategies

  – Informed (Heuristic) Search Strategies

  – Heuristic Functions

Module 4: Search in Complex Environments

– Local Search and Optimization Problems

  – Local Search in Continuous Spaces

– Search with Nondeterministic Actions

– Searching Partially Observable Environments

– Online Search Agents and Unknown Environments

Module 5: Adversarial Search and Games

– Game Theory

– Optimal Decisions in Games

– Heuristic Alpha–Beta Tree Search

– Monte Carlo Tree Search

– Stochastic Games

– Partially Observable Games

– Limitations of Game Search Algorithms

Module 6: Constraint Satisfaction Problems (CSPs)

– Defining Constraint Satisfaction Problems

– Constraint Propagation and Inference in CSPs

– Backtracking Search for CSPs

– Local Search for CSPs

– Structure of Problems

Module 7: Logical Agents

– Knowledge-Based Agents

– The Wumpus World

– Introduction to Logic

  – Propositional Logic: Basics and Theorem Proving

  – Effective Propositional Model Checking

  – Agents Based on Propositional Logic

Module 8: First-Order Logic

– Syntax and Semantics of First-Order Logic

– Using First-Order Logic

– Knowledge Engineering in First-Order Logic

Module 9: Inference in First-Order Logic

– Comparison: Propositional vs. First-Order Inference

– Unification and First-Order Inference

– Forward Chaining

– Backward Chaining

– Resolution

Module 10: Knowledge Representation

– Ontological Engineering

– Categories and Objects

– Events and Mental Objects

– Modal Logic

– Reasoning Systems for Categories

– Reasoning with Default Information

Module 11: Automated Planning

– Classical Planning Definitions and Algorithms

– Heuristics for Planning

– Hierarchical Planning

– Planning and Acting in Nondeterministic Domains

– Time, Schedules, and Resources

– Analysis of Planning Approaches

Module 12: Quantifying Uncertainty

– Acting Under Uncertainty

– Basic Probability Notation

– Inference Using Full Joint Distributions

– Independence and Bayes’ Rule

– Naive Bayes Models

– Revisiting the Wumpus World

Module 13: Probabilistic Reasoning

– Representing Knowledge in Uncertain Domains

– Semantics of Bayesian Networks

– Exact and Approximate Inference in Bayesian Networks

– Causal Networks

Module 14: Probabilistic Reasoning over Time

– Time and Uncertainty

– Inference in Temporal Models

– Hidden Markov Models

– Kalman Filters

– Dynamic Bayesian Networks

Module 15: Probabilistic Programming

– Relational Probability Models

– Universe Probability Models

– Tracking Complex Worlds

– Programs as Probability Models

Module 16: Making Simple Decisions

– Combining Beliefs and Desires under Uncertainty

– Basis of Utility Theory

– Utility Functions and Multiattribute Utility Functions

– Decision Networks

– The Value of Information

– Unknown Preferences

Module 17: Making Complex Decisions

– Sequential Decision Problems

– Algorithms for Markov Decision Processes (MDPs)

– Bandit Problems

– Partially Observable MDPs

– Algorithms for Solving POMDPs

Module 18: Multiagent Decision Making

– Properties of Multiagent Environments

– Non-Cooperative Game Theory

– Cooperative Game Theory

– Collective Decision-Making

Module 19: Learning from Examples

– Forms of Learning

  – Supervised Learning

  – Learning Decision Trees

  – Model Selection and Optimization

– Theory of Learning

– Linear Regression and Classification

– Nonparametric Models

– Ensemble Learning

– Developing Machine Learning Systems

Module 20: Learning Probabilistic Models

– Statistical Learning

– Learning with Complete Data

– Learning with Hidden Variables: The EM Algorithm

Module 21: Deep Learning

– Simple Feedforward Networks

– Computation Graphs for Deep Learning

– Convolutional Networks

– Learning Algorithms and Generalization

– Recurrent Neural Networks

– Unsupervised Learning and Transfer Learning

– Applications

Module 22: Reinforcement Learning

– Learning from Rewards

– Passive Reinforcement Learning

– Active Reinforcement Learning

– Generalization in Reinforcement Learning

– Policy Search

– Apprenticeship and Inverse Reinforcement Learning

– Applications of Reinforcement Learning

Module 23: Natural Language Processing (NLP)

– Language Models

– Grammar and Parsing

– Augmented Grammars

– Complications of Real Natural Language

– NLP Tasks

Module 24: Deep Learning for NLP

– Word Embeddings

– Recurrent Neural Networks for NLP

– Sequence-to-Sequence Models

– Transformer Architecture

– Pretraining and Transfer Learning

– State-of-the-Art Techniques

Module 25: Computer Vision

– Introduction to Computer Vision

– Image Formation and Simple Image Features

– Image Classification and Object Detection

– Understanding the 3D World

– Applications of Computer Vision

Module 26: Robotics

– Overview of Robots and Robot Hardware

– Problem-Solving in Robotics

– Robotic Perception

– Planning and Control

– Planning Uncertain Movements

– Reinforcement Learning in Robotics

– Human-Robot Interaction

– Alternative Robotic Frameworks

– Application Domains

Module 27: Philosophy, Ethics, and Safety of AI

– Limits of AI

– Can Machines Really Think?

– Ethics of AI

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