Course syllabus for Algorithms for machine learning and inference

The course syllabus contains changes
See changes

Course syllabus adopted 2023-02-04 by Head of Programme (or corresponding).

Overview

  • Swedish nameAlgoritmer för maskininlärning och slutledning
  • CodeTDA233
  • Credits7.5 Credits
  • OwnerMPALG
  • 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 02118
  • Maximum participants120
  • Block schedule
  • Open for exchange studentsNo
  • Only students with the course round in the programme overview.

Credit distribution

0120 Written and oral assignments 3 c
Grading: TH		 3 c
0220 Examination 4.5 c
Grading: TH		 4.5 c
  • 15 Mar 2024 am J DIG
  • 07 Jun 2024 am J DIG
  • 28 Aug 2024 am J DIG

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

To be eligible to the course, the student should have a bachelor degree. 
In particular, the student must have acquired the following knowledge: 
- 7.5 credits of programming 
- 7.5 credits of data structures
- 7.5 credits of basic probability and statistics
- 7.5 credits of linear algebra
- 7.5 credits of multivariate calculus. 

The course TDA233 cannot be included in a degree which contains (or is based on another degree which contains) the course DAT340/DAT341.

Aim

This course will discuss the theory and application of basic algorithms for machine learning and inference, from an AI perspective. In this context, we consider learning to draw conclusions from given data or experience which results in some model that generalizes these data. Inference is to compute the desired answers or actions based on the model.
Algorithms of this kind are commonly used in for example classification tasks (character recognition, or to predict if a new customer is creditworthy, etc.) and in expert systems (for example for medical diagnosis). A new and commercially important area of application is data mining, where the algorithms are used to automatically detect interesting information and relations in large commercial or scientific databases.
The course intends to give a good understanding of this cross-disciplinary area, with sufficient depth to use and evaluate the available methods and to understand the scientific literature.

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

Knowledge and understanding:
  • explain a representative set of available methods for machine learning
Competence and skills:
  • implement and analyze machine learning algorithms
  • apply sound mathematical principles to the inference of hypotheses from empirical data and models on scientific grounds
Judgement and approach:
  • choose appropriate methods and apply them to specific inference problems, based on a solid understanding of scientific literature in the field
  • evaluate the methods qualitatively and quantitatively, and recognize their strengths as well as their limitations

Content

The following concepts are covered:
  • Supervised Learning: Bayes classifier, Perceptron, Support vector machines, K-nearest neighbor models, Regression, logistic regression;
  • Maximum likelihood estimation and Bayesian methods;
  • Unsupervised Learning: Clustering algorithms, EM algorithm, Mixture models, Model selection, Kernel methods;
  • Deep Learning models such as fully connected neural networks, Convolutional Neural Networks, Recurrent Neural Networks.

Organisation

Lectures and homework assignments.

Literature

See course homepage.

Examination including compulsory elements

The course is examined by assignments and a written hall 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 module:
    • 2023-05-23: Digital exam Changed to digital exam by vana
      [0220 Examination 4,5 credit] Changed to digital examination