Teaching Experience
Kyle Moore
Teaching Experience
- Introduction to Artificial Intelligence (Spring 2025)
- CS 4260 - Vanderbilt University
- Sole Instructor, lecturer, and designer of all course material.
- ~60 students
- Slide decks released publicly on Github
- Topics covered:
- GOFAI
- Goal-directed Search (BFS, DFS, Djikstra, A*, etc.)
- Adversarial Search (Minimax, alpha-beta pruning, Monte Carlo Tree Search)
- Automated Theorem Proving
- Logic-based Planning
- Constraint Satisfaction Problems
- Machine Learning
- Decision Trees
- Neural Networks
- Miscellaneous ML Algorithms (K-NN, SVMs, Naive Bayes, etc.)
- Clustering (K-Means, Hierarchical Clustering, etc.)
- Reasoning and Planning under Uncertainty
- Probability and Bayes Theorem
- Belief Nets
- (Hierarchical Hidden) Markov Markov
- Markov Decision Processes
- Reinforcement Learning (Value Iteration, Policy Iteration, Q-Learning)
- Extra Topics
- AI and Philosophy
- AI Ethics
- GOFAI
Teaching Assistanceship Experience
- Teaching Assistanceships - Ph.D. (* - guest lectures given)
- CS 4260 - Artificial Intelligence (2022F, 2023F, 2024S, 2025F, 2026S*)
- CS 1151 - Computer Ethics (Summer 2023, Summer 2024, Summer 2025)
- CS 3252 - Theory of Automata, Formal Languages, and Computation (2021F, 2023S, 2024F)
- CS 3265 - Databases (Summer 2022, Summer 2023)
- CS 3891 - Computational Creativity (2023S*)
- CS 3892 - Software for Autonomous Vehicles (2022S)
- CS 3270 - Programming Languages (2019F, 2020S, 2020F. 2021S)
- Teaching Assistanceships - M.S.
- Programming Languages
- Analysis of Algorithms