Introduction to Machine Learning
Introduktion till maskininlärning

EIT155F, 7.5 credits

Valid from: Autumn 2016
Decided by: Professor Thomas Johansson
Date of establishment: 2016-12-08

General Information

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

Aim

To give knowledge about the basic theory for Machine Learning -- construction of automatised systems that can learn/gather information from data, for example learn to recognize characters in a hand-written text.

Goals

Knowledge and Understanding

For a passing grade the doctoral student must

• have a good knowledge of the statistical principles used in machine learning
• have knowledge of the disciplinary foundation for the design and analysis of learning algorithms and systems
• demonstrate in-depth knowledge of methods and theories in the field of machine learning.

Competences and Skills

For a passing grade the doctoral student must

• demonstrate abilities to develop learning techniques and systems based on relevant technological issues
• demonstrate the ability to identify, formulate, design, and implement learning components and applications.

Judgement and Approach

For a passing grade the doctoral student must demonstrate the ability to critically evaluate and compare different learning models and learning algorithms for different problem setups and quality characteristics.

Course Contents

Training, testing, generalization, hypothesis spaces. Linear regression and classification. Kernel methods and support vector machines. Graphical models. Mixture models, Expectation Maximization. Variational and sampling methods.

Course Literature

Bishop, C. M: Pattern Recognition and Machine Learning. Springer, 2006. ISBN 9780387310732.

Instruction Details

Types of instruction: Lectures, seminars, project, self-study literature review

Examination Details

Examination formats: Written assignments, seminars given by participants. Compulsory assignments including computer work and written reports. Assignments shall be peer-reviwed and be discussed in the study group.
Grading scale: Failed, pass
Examiner:

Admission Details

Assumed prior knowledge: FMA420 Linear Algebra, FMA430 Calculus in Several Variables, Fourier analysis and theory of linear systems corresponding to FMAF05 Mathematics-Systems and Transforms, and one of the basic courses in Mathematical Statistics, e.g. FMS012.

Further Information

Examiner: Maria Kihl (maria.kihl@eit.lth.se)

Course Occasion Information

Contact and Other Information

Course coordinators: