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Lecture TopicsDeliverablesNotesOptional (supplementary material)
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Week 1Lecture 1Linear AlgebraIntroduction and Logistics
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Lecture 2Matrix Calculus, Probability TheoryLinear Algebra
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Lecture 3Probability and StatisticsProbability Theory [Slides]Python review slides [materials.zip]
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Week 2Lecture 4Linear Regression, Gradient DescentProblem Set 0 (Due June 27 at 11:59 pm PT)Supervised Learning
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Lecture 5Perceptron, Logistic Regression, Newton's MethodGenerative Learning Algorithms
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Lecture 6Exponential Family, Generalized Linear Models
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Lecture 7Gaussian Discriminant Analysis, Naive Bayes
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Week 3Lecture 8Kernel Methods, Support Vector MachineKernel Methods and SVM
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Lecture 9Bayesian Linear Regression, Gaussian ProcessesGaussian ProcessesMultivariate Gaussian Distribution and Properties
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TA LectureDecision Trees
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Week 4Lecture 10Deep Learning - 1Problem Set 1 (Due Mon July 11 11:59 pm PT)Deep LearningBackpropagation
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Lecture 11Deep Learning - 2Regularization and Model selection
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Lecture 12Bias and Variance, Cross-validation, RegularizationBias-Variance Analysis
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Lecture 13Bias Variance Tradeoff, Statistical Learning TheoryStatistical Learning Theory
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Week 5Lecture 14Reinforcement Learning - 1Reinforcement Learning and Control
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Lecture 15Reinforcement Learning - 2
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Week 6Lecture 16K-Means, Mixture of Gaussians, Expectation MaximizationProblem Set 2 (Due Mon July 25 11:59 pm PT)K-means, Mixture of Gaussians (GMM)
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Lecture 17EM Convergence, Factor Analysis
Expectation Maximization (EM), Factor Analysis
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Lecture 18Factor Analysis Intuition, PCA, ICAPCA, ICA
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Week 7Lecture 19Maximum Entropy, Calibration, Proper Scoring Rules
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Lecture 20Variational Inference, EM-Variants, Variational Autoencoder
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Lecture 21Evaluation Metrics
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Week 8Lecture 22Practical Tips, Course RecapProblem Set 3 (Due Mon Aug 8 11:59 pm PT)
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Lecture 23Course Recap
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ExamFinal ExamFinal Exam (Aug 13)
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