Machine Learning CS-433 – 2016

This is the OLD 2016 course website. For the current 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: [email protected]

Instructor Martin Jaggi Instructor Ruediger Urbanke
Office INJ 341 Office INR 116
Phone +41 21 69 37059 Phone +41 21 69 37692
Email [email protected] Email [email protected]
Office Hours By appointment Office Hours By appointment
Teaching Assistant Mohamad Dia Email [email protected] Office INR 140
Teaching Assistant Ksenia Konyushkova Email [email protected] Office BC304
Teaching Assistant Victor Kristof Email [email protected] Office BC204
Teaching Assistant Taylor Newton Email [email protected] Office B1 Geneva
Teaching Assistant Farnood Salehi Email [email protected] Office BC250
Teaching Assistant Benoît Seguin Email [email protected] Office INN 140
Student Assistant Frederik Kunstner Email [email protected]
Student Assistant Fayez Lahoud Email [email protected]
Student Assistant Tao Lin Email [email protected]
Student Assistant Arnaud Miribel Email [email protected]
Student Assistant Vidit Vidit Email [email protected]
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