Exam is Closed Book

COMPSCI 570: Qualifying Exam Syllabus

Topics Covered for Algorithms and Representations for Artificial Intelligence:

- Search
- Uninformed search
- Informed search
- Constraint Satisfaction

- Game Playing
- Minimax
- alpha-beta search
- Introduction to game theory

- Logic and Knowledge Representation
- Propositional logic
- First order logic
- Theorem Proving

- Reasoning under uncertainty
- Probability
- Bayes nets
- Hidden Markov models and tracking

- Planning
- Classical planning
- Decision theory
- Stochastic planning (MDPs)

Prerequisites for COMPSCI 570 (provided for reference):

- Programming skills: You should be able to write and debug programs in a general-purpose programming language such as C, C++, Java, or Python without drama and without handholding. You should be similarly familiar with pseudocode as typically used in computer science, and able to write it, debug it, and translate it to actual code.
- Ability to do short proofs
- Facility with core computer science concepts:
- Computational complexity
- Analysis of algorithms

- Facility with mathematics concepts:
- Some calculus
- Basic Probability and statistics helpful but not required

Reference:

**Artificial Intelligence: A Modern Approach**, Stuart Russell and Peter Norvig

Sample Exams:

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