Course syllabus for Applied machine learning

The course syllabus contains changes
See changes

Course syllabus adopted 2022-02-01 by Head of Programme (or corresponding).

Overview

  • Swedish nameTillämpad maskininlärning
  • CodeDAT341
  • Credits7.5 Credits
  • OwnerMPDSC
  • Education cycleSecond-cycle
  • Main field of studyComputer Science and Engineering, Software Engineering
  • DepartmentCOMPUTER SCIENCE AND ENGINEERING
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 87125
  • Block schedule
  • Open for exchange studentsYes

Credit distribution

0122 Take-home examination 4 c
Grading: TH		 4 c
0222 Written and oral assignments 3.5 c
Grading: TH		 3.5 c

In programmes

Examiner

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Course round 2

  • Teaching language English
  • Application code 87126
  • Block schedule
  • Open for exchange studentsYes

Credit distribution

0122 Take-home examination 4 c
Grading: TH		 4 c
0222 Written and oral assignments 3.5 c
Grading: TH		 3.5 c

In programmes

Examiner

Go to coursepage (Opens in new tab)

Eligibility

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

A course in programming in a general-purpose language (e.g. C/C++/Java/Python or similar). One course in mathematics (e.g. calculus, linear algebra, applied mathematical thinking), and one course in mathematical statistics. The course "Introduction to Data Science and AI" (DAT405) or similar. The course DAT340 cannot be included in a degree which contains (or is based on another degree which contains) the course TDA233.

Aim

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.

Content

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.

Organisation

Lectures, exercise sessions, computer lab sessions.

Literature

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.

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

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.

The course syllabus contains changes

  • Changes to course rounds:
    • 2022-10-05: Block Block C added by Richard Johansson
      [Course round 2]
    • 2022-10-05: Block Block B added by Richard Johansson
      [Course round 1]