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FJL3380 Theoretical Foundations of Machine Learning 6.0 credits

Course offering missing for current semester as well as for previous and coming semesters
Headings with content from the Course syllabus FJL3380 (Spring 2019–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

A preliminary structure is given below: 

Lecture 1. Introduction

Lecture 2. Probably Approximately Correct Framework and Empirical Risk Minimization

Lecture 3. Concentration inequalities

Lecture 4. The Vapnik-Chervonenkis (VC) Theory

Lecture 5. Linear Classification and Regression

Lecture 6. Regularization, Stability and Optimization

Lecture 7. Support Vector Machines and Kernel Methods

Lecture 8. Deep neural networks

Lecture 9. Clustering. Cluster validation and algorithms

Lecture 10. Reinforcement learning: model-free vs model-based algorithms

Lecture 11. Reinforcement learning: function approximation and deep RL

Intended learning outcomes

When completing the course, the student should be able to:

• Derive and apply the essential theoretical tools used in modern machine learning 

  • Concentration of measure in probability theory
  • Stochastic optimization methods
  • VC theory

• Describe the historical development of supervised and unsupervised learning algorithms

• Reflect on the advantages and drawbacks of deep learning

• Describe and explain the basic reinforcement learning algorithms and their modern versions

Course disposition

Lectures on selected topics.

Literature and preparations

Specific prerequisites

No information inserted

Recommended prerequisites

No information inserted




Understanding Machine Leanring: From theory to algorithms, Shalev-Shwartz et al., lecture slides

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

P, F


  • EXA1 - Examination, 6.0 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Other requirements for final grade

Passing Grade based on 72h home exam and final project. 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). 

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted


Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web FJL3380

Offered by

Main field of study

This course does not belong to any Main field of study.

Education cycle

Third cycle

Add-on studies

No information inserted

Postgraduate course

Postgraduate courses at EECS/Decision and Control Systems