IntroML

Kyle Swanson: swansonk@mit.edu

Telegram: https://t.me/ml_sdu_mit

Feedback form: https://goo.gl/forms/MJSSAMGp5Oc4Dcoc2

Introduction

Welcome to IntroML! This four week class will give you a brief, hands-on introduction to some of the most important topics in machine learning. There will be 11 classes in total, with each class consisting of a lecture on a topic followed by a hands-on lab where we will implement some of the machine learning algorithms discussed in lecture. Lecture and lab materials for each day will be released at the beginning of lecture. See below for a list of topics.

Syllabus

Week 1

• Monday: Introduction to Machine Learning
• Tuesday: Linear Classifiers and the Perceptron Algorithm
• Wednesday: Maximum Margin Classifiers and Support Vector Machines

Week 2

• Monday: Non-Linear Classifiers and Kernels
• Tuesday: Ensembles and the Random Forest Algorithm
• Wednesday: Recommender Systems

Week 3

• Monday: Neural Networks I
• Tueday: Neural Networks II
• Wednesday: Convolutional Neural Networks and Recurrent Neural Networks

Week 4

• Monday: Unsupervised Learning
• Tuesday: Reinforcement Learning

References

Material for this course has been adapted from the class 6.036: Introduction to Machine Learning, taught at MIT by Regina Barzilay, Tommi Jaakkola, and Suvrit Sra in the spring of 2016.