Machine Learning

2018-2019

Course Objective

The goal of this course is to present the dominant concepts of machine
learning methods including some theoretical background. We'll cover
established machine learning techniques such as Linear Models, Support
Vector Machines, Decision Trees and Neural Networks, as well as some
statistical techniques to assess and validate machine learning results.

Course Content

Machine Learning is the study of how to build computer systems that
learn from experience. It is a very active subfield of Artificial
Intelligence that intersects with statistics, cognitive science,
information theory, and probability theory. Recently, Machine Learning
has gained great importance for the design of search engines, robots,
and sensor systems, and for the processing of large scientific data
sets. Further applications include handwriting or speech recognition,
image classification, medical diagnosis, stock market analysis,
bioinformatics, etc.

Teaching Methods

The course consists of two parts: a written exam and a practical
assignment. The written exam is supported by lectures (two per week) and
homework assignments (one per week). The practical assignment is
supported by small exercises to help with the relevant technologies, and
informal biweekly presentations. The practical assignment is made in
groups of five.

The course will be taught in English.

Method of Assessment

Exam (50%) and a project report (50%).

Literature

A digital reader will be provided.

Target Audience

3BA, 3CS, 3LI, 3IMM, mBio

Recommended background knowledge

We recommend that students have some prior experience with Linear
Algebra, Calculus (limited to differentiation), and Probability Theory
(or Statistics). 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.

Programming experience, preferably in python, is highly recommended, but
not strictly necessary.

General Information

Course Code X_400154
Credits 6 EC
Period P4
Course Level 300
Language of Tuition English
Faculty Faculty of Science
Course Coordinator dr. P. Bloem
Examiner dr. P. Bloem
Teaching Staff dr. P. Bloem

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Seminar, Lecture
Target audiences

This course is also available as: