CSC421 Intro to AI - Final Study Guide
The final consists of 7 questions (each worth 5 points).
Two of the questions are from material we covered
before the midterm and the remaining 5 are from
material we covered after. I hope you find this study
guide useful
Sections from Textbook:
- 1.1, 1.3, 1.4
- 2.1, 2.2 , 2.4, 2.5
- 3.1, 3.2, 3.3, 3.4 (wout Bidirectional), 3.5, 3.7
- 4.1, 4.2, 4.3, 4.4
- 5.1, 5.2, 5.3, 5.4, 5.5
- 6.1, 6.2, 6.3, 6.4, 6.6
- 7.1, 7.2, 7.3, 7.4, 7.5, 7.7 (wout Circuit-based agents), 7.8
- 8.1, 8.2, 8.3, 8.4, 8.5
- 9.1, 9.2, 9.3 (wout efficient), 9.4 (wout efficient), 9.5 (until
completeness, 9.6
- 10.1, 10.2, 10.3 , 10.5, 10.6
- ----- After Midterm ----------------
- 13.1, 13.2, 13.3, 13.4, 13.5. 13.6, 13.7 (very important chapter
- foundation for remaining chapters)
- 14.1, 14.2, 14.4, 14.5
- 15,1 15.2, 15,3
- 18.1, 18.2, 18.3
- 20.1, 20.2, 20.4, 20.5, 20.6
Important Topics
Types of agents
Problem definition (PEAS)
Uninformed search strategies (DFS, BFS and iterative deepening)
Informed search strategies (Greedy, A*)
Admissible heurisitcs - relaxed problems
Local Search (Hill climbing)
Constraint Satisfaction Problems (Backtracking, variable and value
ordering)
Local search for CSP
Minimax algorithm
Alpha-Beat Pruning
Propositional Logic and Inference (Backward Chaining, Forward Chaining,
Resolution)
First-order Logic and Inference (Reduction to PL, unification, backward
chaining,
forward chaining, resolution)
Situation-Calculus (Frame-Effect axioms, Succesor-State axioms)
Representational frame problem
-----------After Midterm------------
Basic probability (axioms, notation)
Inference using full joint distributions
Independence and Bayes Rule
Bayesian Networks (BN) (representation/notation)
Exact inference in BN (enumeration + basic idea of variable
elimination)
Approximate inference in BN (direct sampling, rejection sampling,
Markov-Chain Monte Caro)
Hidden Markov Models (basic notation + concepts)
HMM inference tasks (filtering, prediction, smoothing, most likely
explanation)
Inductive Learning
Learning Decision Trees
Baysian Learning, Maximum a posteriori (MAP) and Maximum-Likelihood
Maximum-Likelihood estimation of parametric continuous models
Naive Bayes model
Nearest-neighbor Models
Neural Networks (basic notation/concepts)
Kernel Machines (basic idea/concepts)