The final will be held on Tuesday, May 19th from 8am to 11am in 390 Hearst Mining.
The final will be closed notes, books, laptops, and people. However, you may use up to two (two-sided) note sheets of your own design (group design ok but not recommended).
You may also use a basic, non-programmable calculator, which is not required, but which may be helpful. Your TI-86 is not allowed. Neither is your iPhone.
You can download videos of the midterm review session on
Bayes nets,
search,
more search,
CSPs, and
Minimax. Thanks to Gregory Klets for recording these.
Thursday 5/14 11am-1pm in Hearst Mining 390: Nimar will review questions from the Fall 2008 final exam
Friday 5/15 11am-1pm in Soda 310: John will review some important concepts and techniques (MDPs, Bayes nets and classification)
Office hours:
Friday: John 3pm-4pm in Soda 711
Monday: Nick 2pm-5pm in Soda 711
Possible Exam Topics
Note: exam questions will in many cases ask you to extend or combine basic ideas and algorithms from class. Make sure you understand the fundamentals in addition to being able to procedurally execute algorithms. The exam will not test your knowledge of Python, however questions may assume familiarity with the projects (see past exams for examples).
Search:
BFS, DFS, UCS, A*, Greedy search
Search algorithms' strengths and weaknesses
Properties: completeness, optimality
Admissibility and consistency for A* heuristics
Local search
Be able to phrase search problems and create heuristics
Constraint Satisfaction Problems:
Basic definitions and solution with DFS
Forward checking, arc consistency
Conditions under which CSPs are efficiently solvable
Local search for CSPs
Be able to phrase CSPs
Games:
Minimax search
Alpha-beta pruning
Expectimax search
Evaluation function design
Markov Decision Processes:
The minimum expected utilitiy (MEU) principle
Reflex agents and policies
Markov decision process definition
Reward functions, values and q-values
Bellman Equations
Value and policy iteration
Be able to phrase a problem as an MDP
Reinforcement Learning:
Exploration vs exploitation
Model-based learning
TD value learning / Q-learning
Linear value function approximation
Probability:
Joint, conditional and marginal distributions
Independence and conditional independence
Inference from joint distributions
Bayes' Nets:
Representation and semantics
Inferring joint distributions from conditional probability tables
Inference from joint distributions
Conditional independence and d-separation
Variable elimination
Prior sampling, rejection sampling and likelihood weighting