English

If sufficient demand

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.

Introduction and Foundation,

Linear Models,

Deep Neural Networks.

be able to understand the theory of machine learning and the philosophy behind it, as well as the fundamental ideas behind the state-of-the-art machine learning techniques;
be able to analyze and identify the main machine learning principles and clearly present the main ideas within the machine learning and artificial intelligence domains;
be able to analyze, evaluate, and apply state-of-the-art techniques in the machine learning and artificial intelligence domains.

be able to carry out a group project investigating and applying state-of-the-art techniques;
be able to critically read, evaluate and present scientific literature and course material.

Seminars

Project

Oral exam

Seminars given by participants

Failed, pass

Basics of Probability Theory and Statistics, Basics of Linear Algebra and Numerical Methods, Basics of Algorithms and Data Structures, Programming

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

Course coordinator: Amir Aminifar, E-mail: amir.aminifar@eit.lth.se

EIT195F

2021-04-29

Professor Thomas Johansson