MITx-6.86x Machine Learning with Python

Units

Unit Zero: Brief Review of Vectors, Planes, and Optimization

• Homework
• Project

Unit One: Linear Classifiers and generalisations

• Lecture One: Introduction to Machine Learning
• Lecture Two: Linear Classifiers and Perceptron Algorithm
• Lecture Three: Hinge loss, Margin boundaries and Regularization
• Lecture Four: Linear Classification and Generalization
• Homework
• Project One: Sentiment Analysis

Unit Two: Nonlinear Classification, Linear regression, Collaborative Filtering

• Lecture Five: Linear Regression
• Lecture Six: Nonlinear Classification
• Lecture Seven: Recommender Systems
• Homework
• Project Two: Digit recognition (Part 1)

Unit Three Unit 3 Neural networks

• Lecture Eight: Introduction to Feedforward Neural Networks
• Lecture 9. Feedforward Neural Networks, Back Propagation, and Stochastic Gradient Descent (SGD)
• Lecture 10. Recurrent Neural Networks 1
• Lecture 11. Recurrent Neural Networks 2
• Lecture 12. Convolutional Neural Networks
• Homework
• Project 3: Digit recognition (Part 2)

Unit Four Unit 4: Unsupervised Learning

• Lecture 13. Clustering 1
• Lecture 14. Clustering 2
• Lecture 15. Generative Models
• Lecture 16. Mixture Models; EM algorithm
• Homework 4
• Project 4: Collaborative Filtering via Gaussian Mixtures

Unit Five Unit 5: Reinforcement Learning

• Lecture 17: Reinforcement Learning 1
• Lecture 18: Reinforcement Learning 2
• Lecture 19: Applications: Natural Language Processing
• Homework