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Welcome to the course homepage of FJL3380 Theoretical Foundations of Machine Learning.
This advanced PhD course introduces the basic concepts and mathematical ideas of the foundations of the theory of Machine Learning (ML). The course covers some theoretical aspects of learning theory (e.g., VC theory), and the main ML subfields, including supervised learning (linear classification and regression, SVM, and deep learning), unsupervised learning (clustering), and reinforcement learning.
Lecturers: Alexandre Proutiere and Cristian Rojas
Course literature: S. Shalev-Shwartz and S. Ben David. Understanding Machine Learning: From theory to algorithms, Cambridge University Press, 2015.
Keywords: Supervised and unsupervised learning; regression and classification; stochastic optimization; concentration inequalities; VC theory; SVM, deep learning; clustering; reinforcement learning; online stochastic optimization.
Learning outcomes: After the course, the student should be able to:
Prerequisites: Basic knowledge on Linear Algebra, Probability Theory.
Requirements for final pass grade: For passing the course, successful completion of a 72h home exam and a final project are required. The project consists in reading a few recent papers published at relevant conferences (NIPS, ICML) on a selected topic (e.g., on theoretical justification of deep learning), and to write a state-of-the-art report on the topic including historical developments, recent results, and open problems (5 pages double column minimum).
Pace: 2 or 3 lectures will be given per week.
Course material: The full course schedule and lecture slides will become available under the tab Course schedule and material, visible to those registered in the course.
Registration: If you are interested in taking this course, please sign up, by writing your full name and KTH email address at the doodle: https://doodle.com/poll/kebaa3m2fdamzmvh.
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