Introduction to Machine Learning (89511)


Staff:

Lecturer: Yossi Keshet

Teaching assistant: Yossi Adi and Felix Kruek


Course books:

Shai Shalev-Shwartz and Shai Ben-David, "Understanding Machine Learning: From Theory to Algorithms", Cambridge University Press, 2014

Ian Goodfellow and Yoshua Bengio and Aaron Courville, "Deep Learning", MIT Press, 2016.


Class notes:

  1. Lecture 1 - Introduction

  2. Lecture 2 - ERM principle and Perceptron

  3. Lecture 3 - The optimal Bayes classifier and Maximum likelihood estimator

  4. Lecture 4 - PAC learnability and Support Vector Machines

  5. Lecture 5 - Byond ERM: regularization, SRM, and MDL

  6. Lecture 6 - introduction to deep learning. See Ch.4 of Deng

  7. Lecture 7 - Convolutional neural networks (CNN) - slides are from Sandford CS231n course


Tirgul notes:

A discussion group for this course is available on Piazza Q&A.

The submission should be using the Submit system

  1. Tirgul 1 - Introduction

  2. Tirgul 2 - Perceptron and milticlass

  3. Tirgul 3 - Gradient descent and logistic regression


Exercises:

  1. Excercise 1 (RAR format)