About the Exam
The final will be held on Friday, December 19th, starting promptly at 12:30pm and ending at 3:30pm. It will be in 220 Hearst Gym (!).
The final will be closed notes, books, laptops, and people. However, you may use two pages of notes of your own design (group design ok but not recommended). You may also use a basic calculator (meaning not programmable), which will not be required, but which may be helpful.
Practice Exams
Practice exams:
Fall 07 final
Fall 07 midterm
Fall 06 final
Fall 06 midterm 1, (solutions)
Spring 06 midterm, (solutions)
Spring 06 practice midterm, (solutions)
Spring 06 final
Spring 06 practice final, (solutions)
Note that topics covered vary from term to term and so exam topics and focus may vary.
You can also look at old
exams from more distant semesters.
Review Sessions and Office Hours
Primary review sessions: 12/15 and 12/17, both 6-9pm in 120 Latimer. Topics: 12/15 is selected problems from past finals (Alex). 12/17 is a comprehensive review (Dan).
We will hold several office hours between now and the final. Each hour, we will focus on a particular theme for each one. We can, of course, answer general questions during themed office hours, but will focus on the topics listed.
- Search: Wed. 12/17, 12:30pm-2:30 Soda 711 Alcove (Aria)
- CSPs: Fri 12/12, 12:30-3pm Soda 711 Alcove (Dave)
- Games: Mon 12/15 4-6pm(Alex, 283E, normal OH)
- MDPs: Sun 12/14, 2-4pm, Soda 283E (Dave). Tue, 12/16, 2-4pm, Soda 751 (Dave).
- Reinforcement Learning: TBD (Review Handout)
- Bayes' Nets: Wed 12/17 4:30pm in Soda 711 (Percy)
- HMMs: Thu 12/11, 1-3pm Soda 711 Alcove (Dave)
- Classification: Monday 12/8 11am-1.30pm in Soda 711 (Anh); Monday 12/15, 10am-12pm, Soda 751 (Anna - my normal hours)
- General review: Thursday 12/11, 10am-12pm, Soda 751 (Anna - my normal hours); Thursday 12/18, 10am-12pm, Soda 751 (Anna - my normal hours)
Thursday 12/18, 3pm-5pm, Soda 751 (Slav)
Possible Final Topics
The final covers the entire semester, not just the second half!
Search:
- BFS, DFS, UCS, A*, Greedy search
- Search algorithms' strengths and weaknesses
- Properties: completeness, optimality
- Admissibility and heursitics
- Local search
- Basic robot motion planning (configuration spaces, etc)
- Be able to phrase search problems and create heuristics
Constraint Satisfaction Problems:
- Basic definitions and solution with DFS
- Forward checking, arc consistency
- Be able to phrase CSPs
Games:
- Minimax search
- Alpha-beta pruning
- Expectimax search
- Evaluation function design
Decisions:
- The MEU principle
- Reflex agents and policies
- Markov decision processes
- Reward functions
- Bellman Equations
- Value and policy iteration
- Be able to phrase a problem as an MDP
Reinforcement Learning:
- Exploration vs exploitation
- TD value learning / Q-learning
- Linear value function approximation
Bayes Nets:
- Conditional independence / Bayes' ball
- Basics of parameter estimation (maximum likelihood, smoothing)
- Inference by enumeration
- Inference by sampling
- Variable elimination
- Be able to draw an appropriate BN for a domain
- Decision diagrams
- Value of information
Hidden Markov Models:
- Markov chains
- HMMs
- Forward algorithm
- Viterbi algorithm
- Particle filtering
Classification:
- Basic concepts: learning, generalization, overfitting, experimental methodology
- The naive Bayes classifier
- The perceptron classifier
- The MIRA classifier
- The nearest neighbor classifier
- Estimation and smoothing
- Purposes of held-out (validation) data