Coursebooks

Machine learning

CS-433

Lecturer(s) :

Jaggi Martin
Urbanke Rüdiger

Language:

English

Remarque

The first course (September 18) will take place in the Forum of Rolex Learning Center

Summary

Machine learning and data analysis are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and practically implemented.

Content

  1. Basic regression and classification concepts and methods: Linear models, overfitting, linear regression, Ridge regression, logistic regression, and k-NN.
  2. Fundamental concepts: cost-functions and optimization, cross-validation and bias-variance trade-off, curse of dimensionality.
  3. Unsupervised learning: k-Means Clustering, Gaussian mixture models and the EM algorithm.
  4. Dimensionality reduction: PCA and matrix factorization, word embeddings
  5. Advanced methods: generalized linear models, SVMs and Kernel methods, Neural networks and deep learning

Keywords

Learning Prerequisites

Required courses

Recommended courses

Important concepts to start the course

Learning Outcomes

By the end of the course, the student must be able to:

Teaching methods

Expected student activities

Students are expected to:

Assessment methods

Supervision

Office hours Yes
Assistants Yes
Forum Yes

Resources

Virtual desktop infrastructure (VDI)

No

Bibliography

Ressources en bibliothèque
Notes/Handbook

github.com/epfml/ML_course

Websites

In the programs

  • Data Science, 2018-2019, Master semester 1
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Data Science, 2018-2019, Master semester 3
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Electrical and Electronics Engineering, 2018-2019, Master semester 1
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Electrical and Electronics Engineering, 2018-2019, Master semester 3
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Digital Humanities, 2018-2019, Master semester 1
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Digital Humanities, 2018-2019, Master semester 3
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Computer Science, 2018-2019, Master semester 1
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Computer Science, 2018-2019, Master semester 3
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Life Sciences Engineering, 2018-2019, Master semester 1
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Financial engineering, 2018-2019, Master semester 1
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Financial engineering, 2018-2019, Master semester 3
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Management, Technology and Entrepreneurship, 2018-2019, Master semester 1
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Management, Technology and Entrepreneurship, 2018-2019, Master semester 3
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Computational science and Engineering, 2018-2019, Master semester 1
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Computational science and Engineering, 2018-2019, Master semester 3
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Life Sciences and Technologies - master program, 2018-2019, Master semester 1
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Life Sciences and Technologies - master program, 2018-2019, Master semester 3
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Communication Systems - master program, 2018-2019, Master semester 1
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Communication Systems - master program, 2018-2019, Master semester 3
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Biocomputing minor, 2018-2019, Autumn semester
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Data science minor, 2018-2019, Autumn semester
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Computer science minor, 2018-2019, Autumn semester
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Computational Neurosciences minor, 2018-2019, Autumn semester
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Communication systems minor, 2018-2019, Autumn semester
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Electrical Engineering (edoc), 2018-2019
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Computer and Communication Sciences (edoc), 2018-2019
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      7
    • Subject examined
      Machine learning
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks

Reference week

MoTuWeThFr
8-9
9-10
10-11
11-12
12-13
13-14
14-15 INF119
INF2
INJ218
INM202
INR219
15-16
16-17 SG1
17-18
18-19
19-20
20-21
21-22
Lecture
Exercise, TP
Project, other

legend

  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German