Lecture Notes: Introduction to Artificial Intelligence with Python

Overview

• Course: Harvard University exploration of AI concepts and algorithms
• Instructor: Brian Yu
• Topics Covered:
  • Game-playing engines
  • Handwriting recognition
  • Machine translation
  • Graph search algorithms
  • Classification, optimization, reinforcement learning
  • Machine learning, neural networks, natural language processing

Introduction to AI

• Definition and examples of artificial intelligence (AI)
  • AI tasks like recognizing faces, playing games, understanding language
  • Exploring concepts that enable AI
  • Emphasis on AI and Python

Search Problems

• Search Definition: AI finding solutions to problems (e.g., driving directions, game strategies)
• Knowledge Representation: AI's ability to know and infer information
• Uncertainty Handling: Dealing with probability in uncertain events
• Optimization: Maximizing/minimizing functions to achieve goals
• Machine Learning: AI learning from data to improve task performance
• Neural Networks and NLP: Understanding and processing human language

Graph Search Algorithms

• Agent Definition: An entity that perceives and acts in its environment
• State and Transition: Configuration of agent in the environment, transition models
• Goal Test and Path Cost: Determining end states and minimizing/maximizing costs

Search Strategies

• Frontier Strategy: Using data structures like stacks and queues
• Depth-First Search (DFS): Explores paths deeply until dead ends
• Breadth-First Search (BFS): Explores nodes level by level
• Search Problem Example: Maze-solving illustration

Optimization and Path Planning

• Optimization Focus: Cost minimization and achieving goals
• Search Space Representation: Graphical representation of state spaces
• State Spaces in Real World: Examples like driving directions, maze-solving

Advanced Search Algorithms

• Minimax Algorithm: Optimal decision making in adversarial conditions
• Node Data Structures: Tracking states, goals, and transitions
• AI Game Strategies: Tic-tac-toe, chess, and other games

Knowledge-Based AI

• Reasoning with Logic: Deduction and inference in AI
• Propositional Logic: Using symbols and connectives
• Knowledge Representation: Models of knowledge bases for AI

Inference Techniques

• Model Checking: Ensuring all potential worlds confirm knowledge
• Resolution: Simplifying logical sentences for efficient processing
• Proof by Contradiction: Validating entailments via contradiction

Advanced Logic Systems

• First-Order Logic: Enhancing expressiveness beyond propositional logic
• Quantifiers and Predicates: Expressing complex relationships

Probability and Uncertainty

• Probability Theory: Understanding likelihoods and variable distributions
• Bayesian Networks: Modelling dependencies between variables
• Bayes Rule: Calculating conditional probabilities

Probabilistic Inference

• Hidden Markov Models: Managing hidden states and observations
• Sampling Techniques: Approximate inference through sampling
• Prediction and Filtering: Inferring future states based on observations

Optimization Techniques

• Local Search: Focusing on individual nodes to find solutions
• Hill Climbing: Incrementally seeking improvements in state quality
• Simulated Annealing: Exploring solutions through controlled randomness
• Constraint Satisfaction: Solving problems under defined constraints

Applications in AI

• Practical Examples: Facility location, exam scheduling, game solving
• Machine Learning Introduction: Basics of supervised learning and classification

Conclusion

• AI involves various techniques for solving complex problems
• Importance of algorithms for efficient problem-solving
• Understanding AI requires integration of several disciplines and methodologies