CS-E4830 - Kernel Methods in Machine Learning D, 03.03.2021-03.06.2021
This course space end date is set to 03.06.2021 Search Courses: CS-E4830
Topic outline
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Note: All sessions of the course are online, there are no physical lectures or exercise sessions!Contents
Inner product spaces, Kernels, Reproducing kernels, and RKHS. Introductory learning theory and Generalization. Empirical Risk Minimization, Uniform Convergence and Rademacher Complexity. Kernel Ridge Regression and Logistic Regression. Optimization and Duality. Margin-based methods and Support vector machines. Unsupervised learning including clustering, PCA and their kernel variants.
Course position and Prerequisites
Course is MSc level in Machine learning, targeted to 1st/2nd year MSc students in Machine Learning and Computer science. Also suitable for PhD studies.
Prerequisite knowledge:
- The course Machine Learning: Supervised Methods (CS-E4710), or equivalent knowledge, strongly recommended
- Basics of probability and linear algebra
- Python
programming skills are recommended (course material will use Python,
and numpy. There will be an introductory python programming session)
Learning Outcomes
After attending the course, the student knows the basics of kernels, positive-definiteness, and RKHS. The courses also formally introduces the notions of generalization in machine learning by studying the principle of Empirical Risk Minimization and its consistency. The student knows how convex optimization methods can be used to efficiently train kernel-based and large-scale linear models.It is also discussed how to apply kernel based methods for unsupervised learning such as PCA
Course schedule
Note: All sessions of the course are online, there are no physical lectures or exercise sessions.
- Lectures
(online): Pre-recorded lectures will be made available before the lectures scheduled (every Wednesday afternoon).
- Assignments : completed at home, and submitted online.
- Tutorial sessions: Details will posted shortly. Attending the Q&A sessions is voluntary.
- Exam (online): The exam will be open book.
Date (dd.mm.yyyy) Activity Location 03.03.2021 Lecture 1 pre-recorded 10.03.2021 Lecture 2 pre-recorded 11.03.2021
16:15 - 18:00 (EEST)Python Tutorial Zoom session 17.03.2021 Lecture 3 pre-recorded 24.03.2021 Lecture 4 pre-recorded 25.03.2021
16:00 - 18:00 (EEST)Assignement 1
Tutorial SessionZoom session 31.03.2021 Lecture 5 pre-recorded 07.04.2021 Lecture 6 pre-recorded 08.04.2021
16:00 - 16:45 (EEST)Q&A session
for Lecture 5 & 6Zoom 21.04.2021 Lecture 7 pre-recorded 22.04.2021 Tutorial session
for assignment 2Zoom 28.04.2021 Lecture 8 pre-recorded 29.04.2021
( 16:00 - 16:40)Q&A session
for lectures 7 and 8Zoom 05.05.2021
(12:15 - 14:00)Lecture 9 by
Riikka HuusariLive lecture: https://aalto.zoom.us/j/66519899527
(Recording will be available online afterwards)12.05.2021 Lecture 10 Lecture 10 slides - course review for self study The course can be completed by two alternative ways:
- Assignments (max. 50 points) + Exam (max. 50 points) giving a grade 0..5. The assignment part has a weightage of 50% and exam of 50%. In aggregate, lowest passing points total is 40. 85 points will give the grade of 5.
- Exam only (max. 50 points), giving a grade 0...5. 20 points will give the grade 1, 45 points will give the grade of 5.
The better of the resulting two grades will be taken into account.
Course personnel
- Lecturer: Rohit Babbar
- Course assistants: Petrus Mikkola, Tejas Kulkarni, Mohammadreza Qaraei
Language of Instruction - English
Course Material
- Lecture slides and exercises are the examined content
- Further references will be provided during respective lectures
Additional reading
- Shawe-Taylor and Cristianini: Kernel Methods for Pattern Analysis, Cambridge University Press, 2004. Available as ebook: http://site.ebrary.com/lib/aalto/detail.action?docID=10131674
- B. Scholkopf, A. Smola: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. http://books.google.fi/books?isbn=0262194759
- Research papers provided during the course (See Materials).