Module Number

INF3151 		Module Title

Introduction to Machine Learning 		Type of Module

Compulsory
ECTS 6
Work load
- Contact time
- Self study
Workload:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction German and English
Type of Exam

Written Test
Lecture type(s) Lecture, Tutorial
Content

This module is designed to teach basic principles and simple algorithms from the field of statistical learning.
Topics include: different learning problems and approaches to solving them, basic principles of statistical learning (Bayes' theorem, decision theory, basic problems, evaluation of results), simple baseline models from supervised and unsupervised learning (density estimation, classification, clustering), ML in a social context.
Objectives

The students know basic principles and methods of machine learning and are aware of their principal limitations. In the exercises, they have learned to solve small practical problems with the methods covered and to implement corresponding algorithms in practice.
Allocation of credits / grading
Type of Class
Status
SWS
Credits
Type of Exam
Exam duration
Evaluation
Calculation
of Module (%)
Prerequisite for participation INFM1110 Practical Computer Science 1: Declarative Programming,

INFM1120 Practical Computer Science 2: Imperative and Object-Oriented Programming,

INFM2010 Mathematics for Computer Science 3: Advanced Topics
Lecturer / Other Martius
Literature

It is strongly recommended that students have passed the modules INFM1110, INFM1120 und INFM2010 in advance.

Literature:
'Introduction to Machine Learning', 4th Edition, Ethem Alpaydin, MIT Press. Chapters 1-12 and 20.

'Pattern Recognition and Machine Learning' by Christopher Bishop, https://www.microsoft.com/en-us/research/people/cmbishop/prml-book
Last offered Sommersemester 2022
Planned for Sommersemester 2025
Assigned Study Areas BIOINFM2510, INFM, MDZINFM2510, MEINFM3210