The 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
  Genetic search
    Application to the traveling Salesman Problem

Problem formulation
  States, operators, goal criteria
  Rittel and Webber's 10 characteristics of wicked problems

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

Probabilistic reasoning
  Conditional probability
  Priors, likelihoods, and posteriors
  Bayes' rule
  Odds and conversion between odds and probability
  Naive Bayes modeling
  The joint probability distribution
  Marginal probabilities
  Independence of random variables
  Conditional independence
  Use of conditional independence in Bayes Nets

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
  Policy evaluation
  Temporal difference learning
  Q-learning
  Exploration functions for Q-learning
  Parameters alpha and epsilon used in Q-learning

Classification using Naive Bayes classifiers
  Classification using Naive Bayes
  Division by P(E) not necessary for classification
  Laplace smoothing: Adding 1 to counts when estimating P(Ei | Cj): why and how

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.
  The meaning of deep convolutional networks

The Future of AI
  Asimov's three laws of robotics, Kurzweil's "singularity"