| 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
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