Lectures

You can download the lectures here (in PDF format). I will try to upload lectures prior to their corresponding classes.

• 

  Introduction to Artificial Intelligence
  tl;dr: What is AI? How does it impact our lives? The current state of the art.
  [slides] [notes]
• 

  Introduction to Search
  tl;dr: How to formulate intelligent behaviour as search problems?
  [slides] [notes] videos [1]
• 

  Informed Search and Heuristics
  tl;dr: How to perform informed search with the help of heuristics?
  [slides] [notes] videos [1] [2]
• 

  Constraint Satisfaction Problems and Iterative Improvement
  tl;dr: Introduction to CSPs and how to solve them (backtracking, filtering, arc consistency, etc.); Also, iterative improvement (hill climbing, simulated annealing, genetic algorithms, etc.)
  [slides] [notes] videos [1] [2] [3]
• 

  Game - Adversarial Search
  tl;dr: What if you have an opponent? Zero-sum games, minimax, evaluation functions and alpha-beta pruning.
  [slides] [notes] videos [1] [2]
• 

  Game - Expectimax Search
  tl;dr: What if the game is not fully predictable? i.e., there is stochasticity in the process.
  [slides] [notes] videos [1] [2]
• 

  Markov Decision Processes
  tl;dr: How to find optimal policy in an MDP? You'll also learn about following algorithms: Value Iteration, Policy Evaluation, and Policy Iteration (all based on Bellman updated equations).
  [slides] [notes] videos [1] [2] [3] [4] [5] [6]
• 

  Reinforcement Learning
  tl;dr: How to learn while playing? Reinforcement Learning, Q-learning, exploration vs. exploitation, feature-based state representation.
  [slides] [notes] videos [1] [2] [3] [4] [5]