CS 181
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Lecture Recaps
Lecture 1 (Nonparametric Regression)
Lecture 2 (Linear Regression)
Lecture 3 (Probabilistic Regression)
Lecture 4 (Linear Classification)
Lecture 5 (Probabilistic Classification)
Lecture 6 (Model Selection - Frequentist)
Lecture 7 (Model Selection - Bayesian)
Lecture 8 (Neural Networks 1)
Lecture 9 (Neural Networks 2)
Lecture 10 (Support Vector Machine 1)
Lecture 11 (Support Vector Machine 2)
Lecture 12 (Ethics in ML)
Lecture 13 (Clustering)
Lecture 14 (Mixture Models)
Lecture 15 (Nonprobabilistic Embeddings)
Lecture 16 (Topic Models)
Lecture 17 (Graphical Models)
Lecture 18 (Inference in Bayes Nets)
Lecture 19 (Hidden Markov Models)
Lecture 20 (Markov Decision Processes)
Lecture 21 (Reinforcement Learning 1)
Lecture 22 (Reinforcement Learning 2)
Lecture 23 (Final Lecture - Interpretability)
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