Machine Learning

(formerly known as Pattern Classification and Machine Learning)
previous year's website: http://icapeople.epfl.ch/mekhan/pcml15.html


InstructorMartin Jaggi InstructorRuediger Urbanke
OfficeINJ 339OfficeINR 116
Phone+41 21 69 37059Phone+41 21 69 37692
Emailmartin.jaggi@epfl.chEmailruediger.urbanke@epfl.ch
Office HoursBy appointmentOffice HoursBy appointment


Teaching Assistant Mohamad DiaEmailmohamad.dia@epfl.ch OfficeINR 140
Teaching Assistant Ksenia KonyushkovaEmailksenia.konyushkova@epfl.ch OfficeBC304
Teaching Assistant Victor KristofEmailvictor.kristof@epfl.ch OfficeBC204
Teaching Assistant Taylor NewtonEmailtaylor.newton@epfl.ch OfficeB1 Geneva
Teaching Assistant Farnood SalehiEmailfarnood.salehi@epfl.ch OfficeBC250
Teaching Assistant BenoƮt SeguinEmailbenoit.seguin@epfl.ch OfficeINN 140
Student Assistant Frederik KunstnerEmailfrederik.kunstner@epfl.ch
Student Assistant Fayez LahoudEmailfayez.lahoud@epfl.ch
Student Assistant Tao LinEmailtao.lin@epfl.ch
Student Assistant Arnaud MiribelEmailarnaud.miribel@epfl.ch
Student Assistant Vidit ViditEmailvidit.vidit@epfl.ch


LecturesTuesday 8:15 - 10:00 (Room: CE1)
Thursday 8:15 - 10:00 (Room: CE4)
ExercisesThursday 14:15 - 16:00 (Room: INF119,INJ218,INM11,INM202)


Language: English
Credits : 7 ECTS


See the course information.

Special Announcements

  • 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.
  • Labs: Weekly in the following rooms: INF119 (A-E); INJ218 (F-M); INM11 (N-Q); INM202 (R-Z)
  • 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)

Date Topics Covered Exercises Projects
20/9 Introduction
22/9 Linear Regression Lab 1
27/9 Cost Functions
29/9 Optimization Lab 2
04/10 Least Squares, ill-conditioning
06/10 Maximum Likelihood, Overfitting Lab 3
11/10 Cross-Validation
13/10 Bias-Variance decomposition Lab 4
18/10 Classification
20/10 Logistic Regression Lab 5
25/10 Generalized Linear Models
27/10 k-Nearest Neighbor Lab 6
01/11 Support Vector Machines Proj. 1 due 31.10.
03/11 Kernel Regression Lab 7
08/11 Unsupervised Learning
10/11 k-Means Lab 8
15/11 Gaussian Mixture Models
17/11 EM algorithm Lab 9
22/11 Matrix Factorizations
24/11 Recommender Systems Lab 10
29/11 SVD and PCA
01/12 SVD and PCA Lab 11
06/12 Neural Networks
08/12 Multi-Layer Perceptron Lab 12
13/12 Neural Networks, CNNs
15/12 Decision Trees, Random Forests Lab 13
20/12 BayesNet and Belief Propagation
22/12 Gaussian Processes Lab 14 Project 2 due

Textbook

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

Last modified:: %2016/%09/%13 %22:%Sep