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Coursebooks
Advanced machine learning
MICRO-570
Lecturer(s) :
Billard AudeLanguage:
English
Remarque
pas donné en 2018-19Summary
This course will present some of the core advanced methods in the field for structure discovery, classification and non-linear regression. This is an advanced class in Machine Learning; hence, students are expected to have some background in the field.Content
The class will be accompanied by practical session on computer, using the mldemos software (http://mldemos.epfl.ch) that encompasses more than 30 state of the art algorithms.
- Introduction to the major mathematical principles of Machine Learning
- Structure Discovery: spectral and kernel methods, kernel PCA.CCA, X-means
- Advanced Nonlinear Regression Methods
- Stochastic Modeling: Particle Filters, Reinforcement Learning and Gradient Methods
Keywords
Machine learning, statistics
Learning Prerequisites
Required courses
Probability & Statistics, Linear Algebra
Recommended courses
Machine Learning, Pattern Recognition
Important concepts to start the course
Linear Algebra: Eigenvalue and singular value decomposition
Statistics: Definitions of probability density function, marginal, likelihood, covariance, correlation
Optimization: Lagrange multipliers, gradient descent, local and global optima
Learning Outcomes
By the end of the course, the student must be able to:- Choose an appropriate method
- Apply the method properly
Transversal skills
- Use a work methodology appropriate to the task.
- Write a scientific or technical report.
Teaching methods
Ex-cathedra lectures, exercises, computer-based practical sessions
Expected student activities
Each week, students should read the selected chapters of the Lecture Notes prior to class.
Students must attend the computer-based practice session and prepare regular reports that are graded.
Assessment methods
50% personal work during semester, 50% oral exam
Supervision
Office hours | No |
Assistants | Yes |
Forum | No |
Resources
Ressources en bibliothèque
Notes/Handbook
Machine Learning Techniques, available at the Librairie Polytechnique. To be purchased before the class starts.
Websites
Moodle Link
Prerequisite for
Students must be knowledgeable about machine learning and have taken a course in the area either at EPFL or elsewhere. Relevant courses at EPFL are:
Applied Machine Learning - MICRO-455
Pattern Classification and Machine Learning: CS-433
Data Analysis and Model Classification - EE-516
In the programs
- Energy Management and Sustainability, 2018-2019, Master semester 2
- SemesterSpring
- Exam formOral
- Credits
4 - Subject examined
Advanced machine learning - Lecture
2 Hour(s) per week x 14 weeks - Exercises
1 Hour(s) per week x 14 weeks - Project
1 Hour(s) per week x 14 weeks
- Semester
- Energy Management and Sustainability, 2018-2019, Master semester 4
- SemesterSpring
- Exam formOral
- Credits
4 - Subject examined
Advanced machine learning - Lecture
2 Hour(s) per week x 14 weeks - Exercises
1 Hour(s) per week x 14 weeks - Project
1 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formOral
- Credits
4 - Subject examined
Advanced machine learning - Lecture
2 Hour(s) per week x 14 weeks - Exercises
1 Hour(s) per week x 14 weeks - Project
1 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formOral
- Credits
4 - Subject examined
Advanced machine learning - Lecture
2 Hour(s) per week x 14 weeks - Exercises
1 Hour(s) per week x 14 weeks - Project
1 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formOral
- Credits
4 - Subject examined
Advanced machine learning - Lecture
2 Hour(s) per week x 14 weeks - Exercises
1 Hour(s) per week x 14 weeks - Project
1 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formOral
- Credits
4 - Subject examined
Advanced machine learning - Lecture
2 Hour(s) per week x 14 weeks - Exercises
1 Hour(s) per week x 14 weeks - Project
1 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formOral
- Credits
4 - Subject examined
Advanced machine learning - Lecture
2 Hour(s) per week x 14 weeks - Exercises
1 Hour(s) per week x 14 weeks - Project
1 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formOral
- Credits
4 - Subject examined
Advanced machine learning - Lecture
2 Hour(s) per week x 14 weeks - Exercises
1 Hour(s) per week x 14 weeks - Project
1 Hour(s) per week x 14 weeks
- Semester
Reference week
Mo | Tu | We | Th | Fr | |
---|---|---|---|---|---|
8-9 | |||||
9-10 | |||||
10-11 | CM1120 | ||||
11-12 | |||||
12-13 | |||||
13-14 | |||||
14-15 | |||||
15-16 | ELD020 | ||||
16-17 | |||||
17-18 | |||||
18-19 | |||||
19-20 | |||||
20-21 | |||||
21-22 |
legend
- Autumn semester
- Winter sessions
- Spring semester
- Summer sessions
- Lecture in French
- Lecture in English
- Lecture in German