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Elements of Machine Learning EML

General

study semester
5
standard study semester
6
cycle
every winter semester
duration
1 semester
SWS
4
ECTS
6
teaching language
English

People

responsible
Prof. Dr. Jilles Vreeken
Prof. Dr. Isabel Valera
lectures
Prof. Dr. Jilles Vreeken
Prof. Dr. Isabel Valera

Assessment & Grades

entrance requirements

The lecture assumes basic knowledge in statistics, linear algebra, and programming. It is advisable to have successfully completed Mathematics for Computer Scientists 2 and Statistics Lab. The exercises use the programming language R. We will give a basic introduction to R in the first tutorial. In addition, for preparation the following materials are useful: R for Beginners by Emmanuel Paradis (especially chapters 1, 2, 3 and 6) and An introduction to R (Venables/Smith).

assessment / exams

Prerequisite for admission to the examination is a cumulative 50% of the points of the theoretical and a cumulative 50% of the points of the practical tasks on the exercise sheets. Depending on the number of participants, the examinations are either written or oral. The final modality will be announced in the first two weeks of the lecture.

grade

Will be determined from performance in exams.

Workload

course type /weekly hours
  2 h lectures
+ 2 h tutorial
= 4 h (weekly)
total workload
   60 h of classes
+ 120 h private study
= 180 h (= 6 ECTS)

Aims / Competences to be developed

In this course we will discuss the foundations – the elements – of machine learning. In particular, we will focus on the ability of, given a data set, to choose an appropriate method for analyzing it, to select the appropriate parameters for the model generated by that method and to assess the quality of the resulting model. Both theoretical and practical aspects will be covered. What we cover will be relevant for computer scientists in general as well as for other scientists involved in data analysis and modeling.

Content

The lecture covers basic machine learning methods, in particular the following contents:

  • Introduction to statistical learning
  • Overview over Supervised Learning
  • Linear Regression
  • Linear Classification
  • Splines
  • Model selection and estimation of the test errors
  • Maximum-Likelihood Methods
  • Additive Models
  • Decision trees
  • Boosting
  • Dimensionality reduction
  • Unsupervised learning
  • Clustering
  • Visualization

Literature & Reading

The course broadly follows the book An Introduction to Statistical Learning with Applications in R, Springer (2013). In some cases, the course receives additional material from the book The Elements of Statistical Learning, Springer (second edition, 2009). The first book is the introductory text, the second covers more advanced topics. Both books are available as free PDFs. Any change of, or additional material will be announced before the start of the course on the course webpage.

Additional Information

Curriculum

This module is part of the following study programmes:

Visual Computing MSc: Image Related Fields
study semester: 3 / standard study semester: 4
Cybersicherheit BSc: Grundlagen der Informatik
study semester: 3 / standard study semester: 6
Informatik BSc: Grundlagen der Informatik
study semester: 4 / standard study semester: 6
Medieninformatik BSc: Grundlagen der Informatik
study semester: 4 / standard study semester: 6
Lehramtsstudienfach Informatik: Wahlpflichtbereich 2
study semester: / standard study semester: 5-7
Data Science and Artificial Intelligence BSc: Spezialisierter Bereich DSAI
study semester: 3 / standard study semester: 6
Computer Science BSc (English): Grundlagen der Informatik
study semester: 5 / standard study semester: 6
Cybersecurity BSc (English): Grundlagen der Informatik
study semester: 5 / standard study semester: 6