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389.202 Introduction to Machine Learning with Applications
Diese Lehrveranstaltung ist in allen zugeordneten Curricula Teil der STEOP.
Diese Lehrveranstaltung ist in mindestens einem zugeordneten Curriculum Teil der STEOP.

2019W, VU, 3.0h, 5.0EC, wird geblockt abgehalten

Merkmale

  • Semesterwochenstunden: 3.0
  • ECTS: 5.0
  • Typ: VU Vorlesung mit Übung

Lernergebnisse

Nach positiver Absolvierung der Lehrveranstaltung sind Studierende in der Lage grundlegende Konzepte des machine learnings zu verstehen. Dazu werden vielelerlei Anwendungsbeispiele gegeben, an denen die Stundenten sich selbst versuchen können.

Inhalt der Lehrveranstaltung

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.

Methoden

applied linear algebra

Prüfungsmodus

Mündlich

Weitere Informationen

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.

 

Erste Vorlesung: Freitag , 4.10.2019, 15:00 - 16:45 Uhr, 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


Vortragende

Institut

LVA Termine

TagZeitDatumOrtBeschreibung
Fr.15:00 - 17:0004.10.2019 - 29.11.2019EI 1 Petritsch HS Introduction to Machine Learning with Applications
Mi.15:00 - 17:0009.10.2019 - 30.10.2019EI 2 Pichelmayer HS Introduction to Machine Learning with Applications
Mi.15:00 - 17:0009.10.2019 - 27.11.2019Sem 389, CG0118 Introduction to Machine Learning with Applications
Mi.15:00 - 17:0013.11.2019EI 2 Pichelmayer HS Introduction to Machine Learning with Applications
Mi.15:00 - 17:0027.11.2019EI 2 Pichelmayer HS Introduction to Machine Learning with Applications
Introduction to Machine Learning with Applications - Einzeltermine
TagDatumZeitOrtBeschreibung
Fr.04.10.201915:00 - 17:00EI 1 Petritsch HS Introduction to Machine Learning with Applications
Mi.09.10.201915:00 - 17:00Sem 389, CG0118 Introduction to Machine Learning with Applications
Mi.09.10.201915:00 - 17:00EI 2 Pichelmayer HS Introduction to Machine Learning with Applications
Fr.11.10.201915:00 - 17:00EI 1 Petritsch HS Introduction to Machine Learning with Applications
Mi.16.10.201915:00 - 17:00Sem 389, CG0118 Introduction to Machine Learning with Applications
Mi.16.10.201915:00 - 17:00EI 2 Pichelmayer HS Introduction to Machine Learning with Applications
Fr.18.10.201915:00 - 17:00EI 1 Petritsch HS Introduction to Machine Learning with Applications
Mi.23.10.201915:00 - 17:00Sem 389, CG0118 Introduction to Machine Learning with Applications
Mi.23.10.201915:00 - 17:00EI 2 Pichelmayer HS Introduction to Machine Learning with Applications
Fr.25.10.201915:00 - 17:00EI 1 Petritsch HS Introduction to Machine Learning with Applications
Mi.30.10.201915:00 - 17:00Sem 389, CG0118 Introduction to Machine Learning with Applications
Mi.30.10.201915:00 - 17:00EI 2 Pichelmayer HS Introduction to Machine Learning with Applications
Mi.06.11.201915:00 - 17:00Sem 389, CG0118 Introduction to Machine Learning with Applications
Fr.08.11.201915:00 - 17:00EI 1 Petritsch HS Introduction to Machine Learning with Applications
Mi.13.11.201915:00 - 17:00Sem 389, CG0118 Introduction to Machine Learning with Applications
Mi.13.11.201915:00 - 17:00EI 2 Pichelmayer HS Introduction to Machine Learning with Applications
Mi.20.11.201915:00 - 17:00Sem 389, CG0118 Introduction to Machine Learning with Applications
Fr.22.11.201915:00 - 17:00EI 1 Petritsch HS Introduction to Machine Learning with Applications
Mi.27.11.201915:00 - 17:00Sem 389, CG0118 Introduction to Machine Learning with Applications
Mi.27.11.201915:00 - 17:00EI 2 Pichelmayer HS Introduction to Machine Learning with Applications
LVA wird geblockt abgehalten

Leistungsnachweis

oral exam

LVA-Anmeldung

Von Bis Abmeldung bis
02.10.2019 04:00 14.11.2019 10:00

Curricula

StudienkennzahlSemesterAnm.Bed.Info
710 FW Freie Wahlfächer - Elektrotechnik

Literatur

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.

Vorkenntnisse

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

Sprache

Englisch