Course syllabus for Introduction to machine learning

Course syllabus adopted 2022-02-14 by Head of Programme (or corresponding).

Overview

  • Swedish nameIntroduktion till maskininlärning
  • CodeEEN175
  • Credits6 Credits
  • OwnerTKAUT
  • Education cycleFirst-cycle
  • Main field of studyAutomation and Mechatronics Engineering, Computer Science and Engineering
  • DepartmentELECTRICAL ENGINEERING
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language Swedish
  • Application code 47132
  • Maximum participants150
  • Block schedule
  • Open for exchange studentsNo

Credit distribution

0122 Examination 4.5 c
Grading: TH		4.5 c
  • 27 Okt 2022 am J
  • 03 Jan 2023 am J
  • 15 Aug 2023 am J
0222 Written and oral assignments 1.5 c
Grading: UG		 1.5 c

In programmes

Examiner

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Eligibility

General entry requirements for bachelor's level (first 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

The same as for the programme that owns the course.
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

Basic mathematical and programming skills, and a course in mathematical statistics.Kursens syfte är att introducera och ge grundläggande kunskaper i maskininlärning, med fokus på användning av databaserad inlärning i praktiska tillämpningar.

Aim

The course aims to give an introduction and basic knowledge in machine learning, focusing on how to use data-based learning in practical applications.

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

* explain and apply basic machine learning methods,
* use machine learning software in practical applications, 
* evaluate applicability and limitations of the methods presented in the course.

Content

Probability theory and statistics. Linear regression based on least-square and maximum-likelihood criteria. Classification by distance-based method, the k-nearest neighbors method, decision trees, and logistic regression. Model validation, the bias-variance trade off, and overfitting. Nonlinear parametric learning models, regularization,  and gradient-based optimization. Neural networks and deep learning. Nonlinear transformations, support vector regression and classification. Bayesian linear regression and Gaussian processes. Supervised and unsupervised learning algorithms.

Organisation

Lectures, exercises, and mandatory home assignments.

Literature

Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten, Thomas B. Schön
Machine Learning - A First Course for Engineers and Scientists
2022, Cambridge University Press. Available online.

Examination including compulsory elements

Examination is based on a written exam, as well as passed hand-ins.

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.