Final Examination
CSE 415: Introduction to Artificial Intelligence
The University of Washington, Seattle, Winter 2023
Date: Tuesday, March 14 (2:30-4:20 PM)
Format: The format of the final exam will be similar to that of the midterm exam. However, the exam will be longer. The topics covered will be drawn from the following, which includes some topics from the first part of the course and some from the second.
Topics:
Turing Tests

State-space search
  States, operators, combinatorial explosion
  Admissible heuristics, Consistent heuristics
  Domination of one heuristic over another
  A* search

Minimax search for 2-player, zero-sum games
  Static evaluation functions
  Alpha-beta pruning
  Expectimax search

Markov Decision Processes
  States, actions, transition model, reward function
  Values, Q-states, and Q-values
  Bellman updates
  Policies, policy extraction, number of possible policies
  Value Iteration

Reinforcement Learning
  Q-learning
  Application to the Towers-of-Hanoi puzzle and Grid Worlds

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

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
  Multi-layer perceptrons with soft thresholds (e.g.,
    standard logistic function)
  Autoencoders

Markov Models
  Transition probabilities
  Belief vectors
  The Mini Forward algorithm
  Stationary distribution
  Hidden Markov Model structure
  Using Bayes Rule to update belief when an observation is made

Natural Language Processing
  Probabilistic Context-Free Grammars
    Scoring parses with negative log probabilities
  Representing documents using vectors based on bag-of-words modeling

The Future of AI
  Asimov's three laws of robotics, Kurzweil's "singularity"
  The Trolley Problem,
  Empathy as a goal for ethical AI.  Bias in ML -- esp. facial recognition
  Pros and cons of advanced artificial intelligence
  Superintelligence and Bostrom's concept of singleton