**Staff:**

Lecturer: Dr. Yossi Keshet

Teaching assistant: Tzeviya Fuchs

**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:**

Lecture 1 - Introduction

Lecture 2 - ERM principle and finite hypothesis set

Lecture 3 - PAC model and uniform convergence

Lecture 4 - VC dimension

Lecture 5 - The foundamental theorem of PAC learning; non-uniform convergence: minimum description length (MDL), strcutured risk minimization (SRM)

Lecture 6 - Support vector machine (SVM). Code: svm_vs_perceptron.py

Lecture 7 - SVM and kernels svm_vs_perceptron.py kernel_perceptron.py

Lecture 8 - Maximum likelihood estimation (MLE) and logistoc regression (LR)

Lecture 9 - introduction to deep learning

Lecture 10 - search engines and ranking

**Tirgul notes:**

Tirgul 1 - Introduction

Tirgul 2 (1) and Tirgul 2 (2) - Empirical Risk Minimization Algorithms: Consistent and Halving, optimal Bayes

Tirgul 3 - Perceptron

Tirgul 4 - Perceptron bounds Lecture Notes

Tirgul 5 - Decision Trees

Tirgul 6 - Optimization, Convexity and
Gradient Descent

Tirgul 7 (part 1) and Tirgul 7 (part 2). The demo that was used in the tirgul.

Tirgul 8 - Multiclass classification

Tirgul 9 - Neural networks

Tirgul 10 - Boosting

Tirgul 11 - Multilabel and ranking

Tirgul 12 - Hazara

**Exercises:**

Exercise 1 -- Due: December 1, 2016, solution

Exercise 2 and it's dataset -- Due: December 22, 2016, solution

Exercise 3 (corrected 26.12.12) and it's dataset -- Due: January 1, 2017

Exercise 4 and it's dataset -- Due: January 16, 2017, solution

Exercise 5 and it's dataset -- Due: January 24, 2017

Exercise 6 and it's scripts -- Due: February 15, 2017

The following script iml2016_exercise06_tester.py is the test that runs you code upon submission. It is given for your convenience, review it and see the API and how your final code will be tested. In order to run it locally, place it in the same directory as your code and run normally, please make sure you pass this test to avoid deductions later.

**Exercise grades**

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**Exams:**

2014 Semester A Moed A

2014 Semester A Moed B

2014 Semester B Moed A

2014 Semester B Moed B

2015 Semester A Moed A

2015 Semester A Moed B

2015 Semester B Moed A

2015 Semester B Moed B