Model Map

One page per model, architecture or toolkit: what problem it solves, the techniques inside it, worked examples, what to read in the book, and a practice prompt. Pick a module.

𝛽CS5100Data Analysis

Statistical learning with ISLP: one page per model (KNN, linear and ridge regression, logistic regression, LDA, naive Bayes, trees, neural networks) and the techniques inside it.

18 pagesWeeks 1–4ISLP (James, Witten, Hastie, Tibshirani, Taylor)

Newest: week 4Regression trees Classification trees Tree pruning Neural network Neural network classifier Training neural networks

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AgCS5940Intelligent Agents

Agent architectures with Wooldridge: one page per kind of agent (deductive, teleo-reactive, Agent0, BDI, AgentSpeak, subsumption, hybrid, MDPs and Q-learning) plus the LLM practicals.

22 pagesWeeks 1–4Wooldridge, An Introduction to MultiAgent Systems (2nd ed.)

Newest: week 4Markov decision processes Return, value and Bellman Value iteration Q-learning Exploration vs exploitation POMDPs and belief states When RL goes wrong

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