Course Syllabus for

Machine Learning

EIT195F, 7.5 credits

Valid from: Spring 2021
Decided by: Professor Thomas Johansson
Date of establishment: 2021-04-29

General Information

Division: Electrical and Information Technology
Course type: Third-cycle course
Teaching language: English


The course aim is to provide a broad foundation for machine learning theory as well as state-of-the-art machine learning techniques, e.g., deep neural networks.


Knowledge and Understanding

For a passing grade the doctoral student must

Competences and Skills

For a passing grade the doctoral student must

Course Contents

Introduction and Foundation, Linear Models, Deep Neural Networks.

Course Literature

Murphy, Kevin P.: Probabilistic Machine Learning: An introduction. MIT Press, 2021.

Instruction Details

Types of instruction: Seminars, project

Examination Details

Examination formats: Oral exam, seminars given by participants
Grading scale: Failed, pass

Admission Details

Assumed prior knowledge: Basics of Probability Theory and Statistics, Basics of Linear Algebra and Numerical Methods, Basics of Algorithms and Data Structures, Programming

Further Information

Course coordinator: Amir Aminifar, E-mail:

Course Occasion Information

Contact and Other Information

Course coordinators:

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