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Graduate courses

Departments' graduate courses for PhD-students.


Syllabus for

Academic year
DAT440 - Advanced topics in machine learning  
Avancerade teman i maskininlärning
Syllabus adopted 2020-02-20 by Head of Programme (or corresponding)
Owner: MPDSC
7,5 Credits
Grading: TH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Education cycle: Second-cycle
Major subject: Computer Science and Engineering, Software Engineering

Teaching language: English
Application code: 87119
Open for exchange students: No
Block schedule: B
Minimum participants: 10
Maximum participants: 50
Only students with the course round in the programme plan

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0120 Written and oral assignments 3,5c Grading: UG   3,5c    
0220 Examination 4,0c Grading: TH   4,0c   01 Jun 2021 pm J,  16 Aug 2021 pm L

In programs

MPDSC DATA SCIENCE AND AI, MSC PROGR, Year 1 (compulsory elective)


Morteza Chehreghani

  Go to Course Homepage


General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

To be eligible for the course, a student must have passed a minimum of the following courses:
  • 7.5 credits of programming (Python experience desirable but not absolutely required)
  • 7.5 credits of a data structures and/or algorithms
  • 7.5 credits of basic probability and statistics
  • 7.5 credits of calculus
  • 7.5 credits of linear algebra
  • 7.5 credits of basic machine learning (for example TDA233, MVE440, DAT340)


This course will focus on advanced topics in machine learning, in order to provide a deep understading of the modern machine learning areas. The students will learn sophisticated machine learning models which are commonly used in real-world applications, and also will learn how to analyze and understand in depth the advanced machine learning models.

Learning outcomes (after completion of the course the student should be able to)

  • to learn about modern and advanced machine learning methods and analyze them in different situations
  • to read and understand state-of-the-art scientific articles in the field
  • to propose and employ suitable models for the complex machine learning tasks
  • to be prepared for research and development of advanced machine learning methods


- Theoretical machine learning and the computational aspects
- Advanced deep learning (Deep Neural Network) models
- Active learning/Online learning
- Advanced unsupervised learning


Lectures and assignments.


See course homepage.

Examination including compulsory elements

Assignments (for example homeworks or student presentations) and a written hall examination. The grading scale comprises: U, 3, 4, and 5. A passing grade for the entire course requires at least a passing grade for all sub-courses.
To be awarded a higer passing grade for a full course, the student must, in addition, have a higher average on the weighted grades on the sub-courses grades.

Page manager Published: Thu 04 Feb 2021.