MIT · 6.036

Introduction to Machine Learning

机器学习导论

Principles, algorithms, and applications of supervised learning, reinforcement learning, representation, overfitting, and generalization.

Subject：Machine Learning

Level：Undergraduate

Suggested stage：Years 2–3 (inferred)

Public-material edition：2020

Language：English

Videos：Not verified

Assignments / solutions：Available / Not verified

Prerequisites

Introductory Python programming →Single Variable Calculus →Linear algebra →

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▶Lectures, video & readingCourse content in the official sequence13

Week 1: Basics and linear classifiers Week 2: Perceptrons Week 3: Feature representation Week 4: Margin maximization Week 5: Regression Week 6: Neural networks I Week 7: Neural networks II Week 8: Convolutional neural networks Week 9: State machines and Markov decision processes Week 10: Reinforcement learning Week 11: Recurrent neural networks Week 12: Recommender systems Week 13: Decision trees and nearest neighbors

✓Assignments & practiceOpen the matching problem or assignment page25

◆Labs & projectsCourse projects, labs and milestones12

Week 2 lab Week 3 lab Week 4 lab Week 5 lab Week 6 lab Week 7 lab Week 8 lab Week 9 lab Week 10 lab Week 11 lab Week 12 lab Week 13 lab

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