Machine Learning CS-433 – 2016

This is the OLD 2016 course website. For the current 2017 one, see here.

This course is offered jointly with the Information Processing Group. (Course formerly known as Pattern Classification and Machine Learning).
Previous year’s website:

Our contact email:


Instructor Martin Jaggi   Instructor Ruediger Urbanke
Office INJ 341 Office INR 116
Phone +41 21 69 37059 Phone +41 21 69 37692
Email Email
Office Hours By appointment Office Hours By appointment


Teaching Assistant Mohamad Dia Email   Office INR 140
Teaching Assistant Ksenia Konyushkova Email   Office BC304
Teaching Assistant Victor Kristof Email   Office BC204
Teaching Assistant Taylor Newton Email   Office B1 Geneva
Teaching Assistant Farnood Salehi Email   Office BC250
Teaching Assistant Benoît Seguin Email   Office INN 140
Student Assistant Frederik Kunstner Email      
Student Assistant Fayez Lahoud Email      
Student Assistant Tao Lin Email      
Student Assistant Arnaud Miribel Email      
Student Assistant Vidit Vidit Email      


Lectures Tuesday 8:15 – 10:00 (Room: CE1)
  Thursday 8:15 – 10:00 (Room: CE4)
Exercises Thursday 14:15 – 16:00 (Room: INF119,INJ218,INM11,INM202)
Language:   English
Credits :   7 ECTS

For a summary of the logistics of this course, see the course info sheet here (PDF).
(and also here is a link to official coursebook information).

Special Announcements

Some old mock exams: 2014,2015,2016
Final EXAM: Monday January 16th, 2017
A till F (Fountoukidou)            —  SALLE POLYVALENTE  CE 1515 (1st floor) 
F Fuentes =>  H (HWANG)    —  SALLE POLYVALENTE (2nd floor)
I (Imani ) to Q (Quinton)         —  Salle PO 1 (Polydôme) 
R (Radovanovic) to Z (Zoss)  —  CE 1
Recall, closed book, one page of notes allowed (recto-verso), you have 3 hours from 16:15 till 19:15,
no electronic devices of any kind. Please place all your belongings at the entrance or under your desk.
If you need any extra paper, let us know. Only answer on provided space.
  • The new exercise sheet, as well as the solution (code only) for last weeks lab session will typically be made available each tuesday (here and on github).
  • Projects: There will be two group projects during the course.
    • Project 1 counts 10% and is due Oct 31st.
    • Project 2 counts 30% and is due Dec 22nd.
  • All Labs and Projects will be in Python this year. See Lab 1 to get started.
  • Code Repository for Labs, Projects, Lecture notes:
  • Lectures: Clicker: For some active participation in the lectures, please point your browser to this speak-up room
  • Lecture notes: We provide PDF lecture notes here below and also on Nota Bene so you can comment & discuss them.

Detailed Schedule

(tentative, subject to changes)
Annotated lecture notes from each class are made available on github here.

Date Topics Covered Lectures Exercises Projects
20/9 Introduction 01a,01b    
22/9 Linear Regression, Cost functions 01c,01d Lab 1  
27/9 Optimization 02a    
29/9 Optimization   Lab 2  
04/10 Least Squares, ill-conditioning, Max Likelihood 03a,03b   Project 1 details
06/10 Overfitting, Ridge Regression, Lasso 03c,03d Lab 3  
11/10 Cross-Validation 04a    
13/10 Bias-Variance decomposition 04b Lab 4  
18/10 Classification 05a    
20/10 Logistic Regression 05b Lab 5  
25/10 Generalized Linear Models 06a    
27/10 K-Nearest Neighbor 06b Q&A for proj.  
01/11 Support Vector Machines 07a    Proj. 1 due 31.10.
03/11 Kernel Regression 07b Lab 7  
08/11 Unsupervised Learning, K-Means 08a,08b    
10/11 K-Means, Gaussian Mixture Models 08c Lab 8  
15/11        Mock Exam      
17/11 Gaussian Mixture Models, EM algorithm 09a Mock exam&sol. Project 2 details
22/11 Matrix Factorizations 10a    
24/11 Text Representation Learning 10b Lab 10  
29/11 SVD and PCA 11a    
01/12 SVD and PCA/Neural Networks – Basics 12a Lab 11  
06/12 Neural Networks – Representation Power 12b    
08/12 Neural Networks – Backpropagation, Activation Functions 12c,12d Q&A for proj.  
13/12 Neural Networks – CNN, Regularization, Data Augmentation, Dropout 12e,12f    
15/12 Graphical Models — Bayes Nets 13a Lab 13  
20/12 Graphical Models — Factor Graphs 14a, FG    
22/12 Graphical Models — Inference and Sum-Product Algorithm   Lab 14  Project 2 due


Christopher Bishop, Pattern Recognition and Machine Learning
Kevin Murphy, Machine Learning: A Probabilistic Perspective
Shai Shalev-Shwartz, Shai Ben-David, Understanding Machine Learning