10-301 / 10-601Introduction to Machine Learning
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Lecture 1: Course OverviewLecture 2: Machine Learning as Function ApproximationLecture 3: Decision TreesLecture 4: Decision Tree Properties and HyperparametersLecture 5: k-Nearest Neighbor and Model SelectionLecture 6: PerceptronLecture 7: Linear RegressionLecture 8: Optimization for MLLecture 9: Stochastic Gradient Descent / Logistic RegressionLecture 10: Feature Engineering / RegularizationLecture 11: Neural NetworksLecture 12: Backpropagation ILecture 13: Backpropagation IILecture 14: Societal Impacts of MLLecture 15: PAC learningLecture 16: PAC Learning / MLE & MAPLecture 17: MLE & MAP / CNNs and RNNsLecture 18: RNN-LMs and Transformers-LMsLecture 19: Transformers, AutoDiff, Pre-training, Fine-TuningLecture 20: In-context Learning / Reinforcement Learning: MDPsLecture 21: Reinforcement Learning: Value/Policy IterationLecture 22: Reinforcement Learning: Policy Gradient / Deep RLLecture 23: Recommender SystemsLecture 24: Ensemble Methods: Boosting & BaggingLecture 25: K-Means / Dimensionality Reduction: PCALecture 26: Special Topics: Coding Agents / Significance Testing for MLLecture 27: Special Topics: Generative Models for Vision

Classification & Regression › Course Overview

Lecture 1: Course Overview

Lecturer: Pat Virtue & Matt Gormley

Unit: Classification & Regression

Readings: objectives, cmdline-io-tutorial, math-resources

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