Module Number

INF3151 		Module Title

Basics of 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 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
Lecturer / Other Akata, Martius
Literature

The interested students should have passed the lectures INFM1110 or INFM1120 before taking this lecture.

The lecture will follow the book 'Introduction to Machine Learning', 4th Edition, Ethem Alpaydin, MIT Press. It will cover the Chapters 1-12 and 20.
Last offered Sommersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas BIOINFM2510, INFM, MDZINFM2510, MEINFM3210