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Graduate courses

Departments' graduate courses for PhD-students.


Syllabus for

Academic year
DAT340 - Applied Machine Learning  
Tillämpad maskininlärning
Syllabus adopted 2019-02-21 by Head of Programme (or corresponding)
Owner: MPDSC
7,5 Credits
Grading: TH - Five, Four, Three, Fail
Education cycle: Second-cycle
Major subject: Computer Science and Engineering, Information Technology

The course is full. More than 20 students on the waiting list. For waiting list, please contact the director of studies:
Teaching language: English
Application code: 87112
Open for exchange students: No
Block schedule: B
Maximum participants: 65

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0117 Examination 4,0 c Grading: TH   4,0 c   Contact examiner,  Contact examiner,  Contact examiner
0217 Written and oral assignments 3,5 c Grading: TH   3,5 c    

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Richard Johansson

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In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement: Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.

Course specific prerequisites

At least 15 credits of programming and at least 7,5 credits in mathematics (including analysis, statistics, probability theory)


The course gives an introduction to machine learning techniques and theory, with a focus on its use in practical applications.

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

On successful completion of the course the student will be able to:

Knowledge and understanding
  • describe the most common types of machine learning problems,
  • explain what types of problems can be addressed by machine learning, and the limitations of machine learning
  • account for why it is important to have informative data and features for the success of machine learning systems,
  • explain on a high level how different machine learning models generalize from training examples.

Skills and abilities
  • apply a machine learning toolkit in an application relevant to the data science area,
  • write the code to implement some machine learning algorithms,
  • apply evaluation methods to assess the quality of a machine learning system, and compare different machine learning systems.

Judgement and approach

  • discuss the advantages and limitations of different machine learning models with respect to a given task,
  • reason about what type of information or features could be useful in a machine learning task,
  • select the appropriate evaluation methodology for a machine learning system and motivate this choice,
  • reason about ethical questions pertaining to machine learning systems.


During the course, a selection of topics will be covered in supervised learning, such as linear models for regression and classification, or nonlinear models such as neural networks, and in unsupervised learning such as clustering.

The use cases and limitations of these algorithms will be discussed, and their implementation will be investigated in programming assignments. Methodological questions pertaining to the evaluation of machine learning systems will also be discussed, as well as some of the ethical questions that can arise when applying machine learning technologies.

There will be a strong emphasis on the real-world context in which machine learning systems are used. The use of machine learning components in practical applications will be exemplified, and realistic scenarios will be studied in application areas such as ecommerce,
business intelligence, natural language processing, image processing, and bioinformatics. The importance of the design and selection of features, and their reliability, will be discussed.


Lectures, exercise sessions, computer lab sessions.


Course literature to be announced the latest 8 weeks prior to the start of the course.

Examination including compulsory elements

The course is examined by an individual written take-home exam, as well as mandatory written assignments submitted as written reports, some of which will be carried out individually and others in groups of normally 2-4 students.

Late submission of the take-home exam results in the grade Fail (U), unless special reasons exist. A failed take-home exam is reexamined by a new take-home exam.

Page manager Published: Thu 04 Feb 2021.