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 | 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. |
|
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 | 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 |