Turing Test

State-space search
  States, state spaces, operators, preconditions, moves,
  Heuristic evaluation functions,
  Iterative depth-first search, recursive depth-first search,
  Breadth-first search, best-first search, uniform-cost search,
  Iterative deepening, A* search.
  Admissible heuristics, Consistent heuristics

Problem formulation
  States, operators, goal criteria

Minimax search for 2-player, zero-sum games
  Static evaluation functions
  Backed up values
  Alpha-beta pruning
  Expectimax search
  Zobrist Hashing

Markov Decision Processes
  States, actions, transition model, reward function
  Values, Q-states, and Q-values
  Bellman updates
  Policies, policy extraction

Reinforcement Learning
  Model-based vs model-free learning
  Q-learning
  Feature-based Q-learning
  Application to the Towers-of-Hanoi puzzle and Grid Worlds

Probabilistic reasoning
  Conditional probability
  Bayes' rule
  The joint probability distribution
  Marginal probabilities
  Independence of random variables
  Bayes nets
    Representation using graphs, marginals and conditional distributions
    Number of free parameters
    Inference in Bayes nets
    D-Separation in Bayes nets

Markov Models
  Mini-Forward algorithm
  Stationary distribution

Hidden Markov Models
  Viterbi algorithm
  Forward algorithm

Perceptrons
  How to compute AND, OR, and NOT
  Simple pattern recognition (e.g., 5 x 5 binary image
    inputs for optical character recognition)
  Training sets, training sequences, and the perceptron 
    training algorithm
  Linear separability and the perceptron training theorem

Natural Language Processing
  Probabilistic Context-Free Grammars
    Scoring parses with negative log probabilities

The Future of AI
  Asimov's three laws of robotics, Kurzweil's "singularity"
  Pros and cons of advanced artificial intelligence