389.202 Introduction to Machine Learning with Applications
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2019W, VU, 3.0h, 5.0EC, to be held in blocked form
TUWEL

Properties

  • Semester hours: 3.0
  • Credits: 5.0
  • Type: VU Lecture and Exercise

Learning outcomes

After successful completion of the course, students are able to understand teh basic concepts of machine learning. Many application example will be given on which the students can try themselves how to apply the methods.

Subject of course

The goal of this course is to introduce the student to key machine learning concepts and promote the development of technical skills that will enable the application of theory on selected problems from the fields of natural language processing, image analysis and audio processing. The course will mainly adopt a classification perspective and will cover traditional machine learning theory along with more recent trends, reaching up to probabilistic graphical models, dictionary learning and common deep neural networks architectures. Furthermore, clustering methods will be treated as complementary material. The presentation format will be largely based on the explanation of the underlying theory for each topic followed by practical examples in Python and Matlab that highlight the key features of the presented methods. It is assumed that the student has basic knowledge of linear algebra, statistics, function optimization theory and basic programming skills. Paper exams contribute 70% to the final grade and the remaining 30% comes from a programming assignment related to the development of a machine learning based system that analyzes a publicly available dataset.

 

An outline of the course structure is the following: Introduction to Machine Learning, Bayesian Theory fundamentals, Cost Function Optimization and related Classifiers, Neural Networks and Deep Learning, Data Transforms (Feature Generation/Dimensionality Reduction), Feature Selection, Template Matching, Hidden Markov Modeling, Probabilistic Graphical Models, Dictionary Learning and Clustering.

Teaching methods

applied linear algebra

Mode of examination

Oral

Additional information

the course is based on the following text books

 

[1] S. Theodoridis, K. Koutroumbas, “Pattern Recognition”, 4th edition, Academic Press, 2009. 

[2] S. Theodoridis, “Machine Learning: A Bayesian and Optimization Perspective”, Academic Press, 2015.

[3] S. Theodoridis, A. Pikrakis, K. Koutroumbas, D. Cavouras, Academic Press, “Introduction to Pattern Recognition: a MATLAB approach”, 2010.

[4] Christopher M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2006.

 

first class: Fri., 4.10.2019, 15:00 - 16:45, SEM 389, Raum Nr. CG0118

links to matlab files:


https://github.com/pikrakis/Introduction-to-Pattern-Recognition-a-Matlab-Approach

I am mostly using the official slides of the "Pattern Recognition" book by Sergios Theodoridis. They can be downloaded in two parts from the following links:

http://booksite.elsevier.com/9781597492720/appendices/PPTpart1.zip

http://booksite.elsevier.com/9781597492720/appendices/PPTpart2.zip

Lecturers

Institute

Course dates

DayTimeDateLocationDescription
Fri15:00 - 17:0004.10.2019 - 29.11.2019EI 1 Petritsch HS Introduction to Machine Learning with Applications
Wed15:00 - 17:0009.10.2019 - 30.10.2019EI 2 Pichelmayer HS Introduction to Machine Learning with Applications
Wed15:00 - 17:0009.10.2019 - 27.11.2019Sem 389 Introduction to Machine Learning with Applications
Wed15:00 - 17:0013.11.2019EI 2 Pichelmayer HS Introduction to Machine Learning with Applications
Wed15:00 - 17:0027.11.2019EI 2 Pichelmayer HS Introduction to Machine Learning with Applications
Introduction to Machine Learning with Applications - Single appointments
DayDateTimeLocationDescription
Fri04.10.201915:00 - 17:00EI 1 Petritsch HS Introduction to Machine Learning with Applications
Wed09.10.201915:00 - 17:00Sem 389 Introduction to Machine Learning with Applications
Wed09.10.201915:00 - 17:00EI 2 Pichelmayer HS Introduction to Machine Learning with Applications
Fri11.10.201915:00 - 17:00EI 1 Petritsch HS Introduction to Machine Learning with Applications
Wed16.10.201915:00 - 17:00Sem 389 Introduction to Machine Learning with Applications
Wed16.10.201915:00 - 17:00EI 2 Pichelmayer HS Introduction to Machine Learning with Applications
Fri18.10.201915:00 - 17:00EI 1 Petritsch HS Introduction to Machine Learning with Applications
Wed23.10.201915:00 - 17:00Sem 389 Introduction to Machine Learning with Applications
Wed23.10.201915:00 - 17:00EI 2 Pichelmayer HS Introduction to Machine Learning with Applications
Fri25.10.201915:00 - 17:00EI 1 Petritsch HS Introduction to Machine Learning with Applications
Wed30.10.201915:00 - 17:00Sem 389 Introduction to Machine Learning with Applications
Wed30.10.201915:00 - 17:00EI 2 Pichelmayer HS Introduction to Machine Learning with Applications
Wed06.11.201915:00 - 17:00Sem 389 Introduction to Machine Learning with Applications
Fri08.11.201915:00 - 17:00EI 1 Petritsch HS Introduction to Machine Learning with Applications
Wed13.11.201915:00 - 17:00Sem 389 Introduction to Machine Learning with Applications
Wed13.11.201915:00 - 17:00EI 2 Pichelmayer HS Introduction to Machine Learning with Applications
Wed20.11.201915:00 - 17:00Sem 389 Introduction to Machine Learning with Applications
Fri22.11.201915:00 - 17:00EI 1 Petritsch HS Introduction to Machine Learning with Applications
Wed27.11.201915:00 - 17:00Sem 389 Introduction to Machine Learning with Applications
Wed27.11.201915:00 - 17:00EI 2 Pichelmayer HS Introduction to Machine Learning with Applications
Course is held blocked

Examination modalities

oral exam

Course registration

Begin End Deregistration end
02.10.2019 04:00 14.11.2019 10:00

Curricula

Literature

1] S. Theodoridis, K. Koutroumbas, “Pattern Recognition”, 4th edition, Academic Press, 2009. 

[2] S. Theodoridis, “Machine Learning: A Bayesian and Optimization Perspective”, Academic Press, 2015.

[3] S. Theodoridis, A. Pikrakis, K. Koutroumbas, D. Cavouras, Academic Press, “Introduction to Pattern Recognition: a MATLAB approach”, 2010.

[4] Christopher M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2006.

Previous knowledge

solid knowledge in Signal Processing is required, e.g. SP1 and SP2

Language

English