6.036 Introduction to Machine Learning

About This Course

This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization. These concepts are exercised in supervised learning and reinforcement learning, with applications to images and to temporal sequences.

Learning Objectives

• Understand the formulation of well-specified machine learning problems
  
  
• Learn how to perform supervised and reinforcement learning, with images and temporal sequences.
  
  

Format of This Course

This course includes lectures, lecture notes, exercises, labs, and homework problems.

Recommended Prerequisites

Computer programming (python); Calculus; Linear Algebra

• Welcome to 6.036
• Week 1: Basics
• Week 2: Perceptrons
• Week 3: Features
• 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