Artificial Intelligence

• The purpose of this course is to update the student on state-of-the-art artificial intelligence concepts, such as heuristic programming, state-space search techniques, and/or graphs for problem solving, game playing techniques, theorem proving procedures for propositions and first-order logic, knowledge representation, and examples of knowledge-based systems. 

• We covered a lot of general algorithms for AI, and advantages and disadvantages of each algorithm.

• We also spent a lot of time just going over the current AI concepts and what is in use today (As of early 2024), as well as a general idea of how each of them work.

Algorithms

• Exploration of standard algorithm construction methods for solving varied problems including a comparison of the different efficiencies of these algorithms when implemented using different data structures. 

• Methods included branch and bound, backtracking, memoization, greedy algorithms, and dynamic programming. 

• Heuristic-based methods to find non-optimal solutions were also explored, especially for intractable problems. 

Theoretical Computer Science

• Core topics from finite automata, languages and the theory of computation. 

• The Chomsky hierarchy, abstract machines and their associated grammars. 

• Models of computation (e.g., Turing machines), Church's thesis, unsolvability, and undecidability. 

• Computational complexity, intractability, and NP-completeness.