Princeton University · COS 324

Introduction to Machine Learning

机器学习导论

Supervised and unsupervised learning, regularization, neural networks, dimensionality reduction, and reinforcement learning.

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Lecture 1: Machine Learning through Linear Models

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Subject：Machine Learning

Level：Advanced Undergraduate

Suggested stage：Years 3–4 (inferred)

Public-material edition：2020

Language：English

Videos：Not verified

Assignments / solutions：Available / Not available

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Lecture 1: Machine Learning through Linear Models Lecture 2: Linear Regression Lecture 3: Gradient Descent Lecture 4: Logistic and Multiclass Regression Lecture 5: Cross Validation and Interpretability Lecture 6: K-Means Clustering Lecture 7: Dimensionality Reduction and SVD Lecture 8: Probability and N-Gram Models Lecture 9: Evaluating Probabilistic Models Lecture 10: Word Embeddings Lecture 11: Sampling and Generalization Lecture 12: Midterm Review Lecture 13: Deep Neural Networks Lecture 14: Backpropagation Lecture 15: Convolutional Networks Lecture 16: Convolutional Networks Continued Lecture 17: Recurrent Networks Lecture 18: Decision-Making under Uncertainty Lecture 19: Markov Decision Processes Lecture 20: Policy Optimization Lecture 21: Game Playing and AlphaGo Lecture 22: Ethics in Machine Learning Lecture 23: Deep Learning and Language Models Lecture 24: Course Review

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Princeton University Department of Computer Science · verified 2026-07-31

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