Modules

This page shows the list of all the modules, which will be updated as the class progresses. There are three types of modules:
  • [date]: It was covered in class, and you are responsible for the material.
  • offline: It was not covered in class, but you are responsible for the material.
  • optional: It was not covered in class, and you are not responsible for the material.
DateModuleLinksDescription
Prerequisites
offlineLinear algebraVectors, dot products, geometric interpretations.
offlineVector calculusTaking gradients.
offlineProbability 1Discrete random variables and probability distributions, mean, variance (from Khan Academy).
offlineProbability 2Marginal and conditional distributions (from Khan Academy).
offlineComplexityBasic big-Oh notation, complexity.
offlineOptimizationContinuous optimization, objective functions, gradient descent.
offlinePythonTutorial on using Python for this course.
General
April 1IntroductionAI history, ethics and responsibility, and what we are covering in this course.
offlineProjectGuidelines on how to do a project.
Machine learning
April 3Machine Learning 1Linear regression and Linear classification
April 8Machine Learning 2SGD, feature templates, non-linear features, neural networks.
offlineAlgorithms and distributionEthical frameworks related to how algorithms distribute burdens and benefits.
April 10Machine Learning 3Backpropagation, K-means, generalization, and best practices.
Search
April 15Search 1Tree search, Dynamic programming, uniform cost search.
April 17Search 2Uniform cost search, A-star, A-star relaxations, (optional) Structured Perceptron.
offlineDual Use TechnologiesExternalities and Dual Use Technologies
Markov Decision Processes (MDPs)
April 22MDPs 1Modeling, Policy evaluation, Value iteration.
April 24MDPs 2Reinforcement Learning, Monte Carlo, SARSA, Q-learning, exploration/exploitation, function approximation.
offlineAligning RL SystemsAligning Reinforcement Learning Systems with Human Intent.
Games
April 29Games 1Adversarial games, Expectimax, Minimax, Evaluation functions, Alpha-beta pruning.
May 1Games 2TD-learning, Simultaneous games, Non-zero-sum games, Applications.
offlineThe AI Alignment ProblemThe AI Alignment Problem Reward Hacking and Negative Side Effects
Constraint satisfaction problems
May 6Factor GraphsOverview, Definitions, Examples
May 8Exact and Approximate SearchDynamic Ordering, Arc consistency, Beam Search, Local search.
offlineEncoding Human ValuesEncoding Human Values in AI Systems
Markov and Bayesian Networks
May 13Markov Networks and Bayesian Networks 1Markov networks, Gibbs sampling, Bayesian networks definitions.
May 15Bayesian Networks 2Probabilistic programming, Probabilistic inference, Forward backward, Particle filtering.
offlineAI PrivacyOverview and Adversarial Attack Risks
May 20Bayesian Networks 3Supervised learning, Laplace smoothing, Expectation maximization
Logic
May 22Logic 1Syntax versus semantics, Propositional logic, Inference rules.
offlineAI Explainability and InterpretabilityExplainability and Interpretability in AI Systems
May 29Logic 2Modus ponens, Resolution, First order logic.
Conclusion
June 3ConclusionSummary of topics in CS221, future courses, and conclusion.