Machine Learning

Course Objective

Upon completion of this course, students will:

- be acquainted with the dominant concepts of machine learning methods,
including some theoretical background. (Knowledge and understanding)
- acquire knowledge of established machine learning techniques such as
linear models, neural networks, decision trees and ensemble methods
(Knowledge and understanding)
- learn some statistical techniques to assess and validate machine
learning results. (Apply knowledge and understanding, make judgements)

Course Content

Machine learning is the discipline that studies how to build computer programs that learn how to behave from examples, rather than following explicit instructions. It is a subfield of artificial

intelligence that intersects with statistics, cognitive science, information theory, and probability theory.

Recently, machine learning has become increasingly important for the

design of search engines, robots, and sensor systems, and for the

processing of large scientific data sets. Other applications include

handwriting or speech recognition, image classification, medical

diagnosis, stock market analysis and bioinformatics.

The course covers a wide variety of machine learning techniques, but

puts particular emphasis on gradient descent optimization,

backpropagation, neural networks and deep learning. Some discussion on

the broader social impact of machine learning technology is included.

Additional Information Teaching Methods

The course consists of pre-recorded videos, interactive lecture/QA sessions (two per week) and optional homework assignments discussed in working groups (one per week). The practical assignment is supported by small exercises to help with the relevant technologies, and informal presentations at project groups (one per week).

There is no mandatory attendance for any lectures or workgroups, except that one member of each group must be present at the weekly project group. A large amount of the material is freely available at http://mlvu.github.io The course is taught in English.

Method of Assessment

The course assessment consists of two parts: an examination and a practical assignment. The examination consists of a standard exam and four online quizzes. The examination and practical assignment both comprise 50% of the final

grade. To pass the course, the examination grade should be at least 5.5,

the practical assignment grade should be at least 4.5 and the average

should be at

least 5.5.

The examination is made individually, and the practical assignment is

made in groups. There is a resit for the exam, no resit is possible for

the practical assignment.

Literature

There is no textbook. Some reading material will be provided digitally.

Additional Information Target Audience

Bachelor Artificial Intelligence year 2

Bachelor Econometric and Operation Research

Bachelor Business Analytics

Bachelor Computer Science

Master Bioinformatics and Systems Biology

Custom Course Registration

Workgroup registration will be done through Canvas in the first week of
the course. Only a registration for the main course itself is required
to take part in all parts of the course.

Recommended background knowledge

We require that students have some prior experience with linear algebra,

calculus (limited to differentiation), and probability theory or

statistics. An overview and explanation of the required preliminaries can be found at https://mlvu.github.io/preliminaries/

A basic understanding will suffice and we will take some time to go over

the basics again. Feel free to register if you have no experience with

any of these, but expect to put in a little extra effort in the first

weeks (using the materials provided).

Programming experience, preferably in python, is also required.