Machine learning using MATLAB

This one-semester course provides a first introduction to machine learning including the following topics:

  • Linear regression
  • Logistic regression
  • Support vector machines
  • Deep Neural network
  • Dimensionality reduction
  • Clustering
  • Anomaly detection

complemented by a exercise session of practical Matlab exercises. 

Notice:

  • Our lecture room has changed from Z613 to G305!
  • We will have a quiz on 19th December!
  • 19th December:   Deadline to submit your group list and project proposal (Title plus a few sentences to describe your project). Late proposal submission will be rejected!
  • The submission deadline of technical report is 29.03.2019
  • The template for technical report can be downloaded from the link (https://www.ieee.org/conferences/publishing/templates.html), please note that the page limit is 4 pages. 

Presentation schedule

Each group has 30 mins to present their project and 15 mins of Q&A. When you are doing your presentation, you may not finish your model training and performance evaluation, so you are allowed to present your motivation, framework of your approach, data collection, etc. 

No.TopicMemberTime
1Recognizing instruments from an audio track  

Gero Birkhölzer

01.28

2

Using Machine Learning for Generating Piano Notes

Adam Norris, Jan-laun Cieslik

01.28

3Picture generation via audio input Hilmi Can Yumak, Yannick Bosch, Anton Zickenberg01.30
4Predicting NBA Playoffs with Machine Learning Kerem Dönmez, Deniz Dölek

01.30 
5Using News to Predict Stock MovementsLivia Mano, Egi Papa02.04
6Foreign exchange rates prediction using LSTM Recurrent Neural NetworkHuy Phung, Tashi Choden, Sahil Pasricha02.04
7Dog Breed Identification

Denis Makarov, Timo Jockers

02.06
8Position prediction for birds

Wilhelm Kerle, Jannis Denecke Nikolas Schwarz

02.06
9Dog Breed Identification

Julius Rauscher, Julia Klein, Thomas Enderle

02.11
10Whale RecognitionJoobin Youn, Ying Wang, Ziyao He02.11
11Letter localization

Markus Köhler

02.13
12A recommendation system

Perla Gjoka, Klea Koçi, Tea Jani

02.13
13Detect the mood of tweets and facebook posts (Quit)Tolga Tuncer, Valentin Wagner02.18
14Apiary health detectionGemza Ademaj, Souvik Mondal, Gent Ymeri02.18
Lecture in ZEuS Exercise in ZEuS

Prerequisites

None, if taken as a master course. If taken as an advanced course in the bachelor program:

  • basic math courses offered in our bachelor programs
  • algorithms and data structures
  • introduction to computer science including programming

Learning objective

You will

  • have an insight of the fundamentals of Machine Learning
  • have the ability to design your own machine learning algorithm to solve some specific problems with Matlab
  • know how to improve the performance using training data

Lecture slides

Lecture 12

Date: 21.01.19

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Lecture 11

Date: 14.01.19

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Lecture 10

Date: 07.01.19

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Lecture 9

Date: 17.12.18

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Lecture 8

Date: 10.12.18

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Lecture 7

Date: 03.12.18

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Lecture 6

Date: 26.11.18

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Lecture 5

Date: 19.11.18

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Lecture 4

Date: 12.11.18

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Lecture 3

Date: 05.11.18

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Lecture 2

Date: 29.10.18

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Lecture 1

Date: 22.10.18

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Exercise

Clustering

Date: 16.01.19

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Deep learning toolbox

Date: 09.01.19

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Quiz

Date: 19.12.18

Exercise 6

Date: 05.12.18

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Exercise 5

Date: 28.11.18

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Exercise 4

Date: 21.11.18

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Exercise 3

Date: 14.11.18

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Exercise 2

Date: 07.11.18

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Exercise 1

Date: 31.10.18

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Matlab tutorial

Date: 24.10.18

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Reference 

Textbook

  • Christopher M.. Bishop. Pattern recognition and machine learning. Springer, 2006.
  • Alpaydin, Ethem. Introduction to machine learning. MIT press, 2014.
  • Ian Goodfellow, Yoshua Bengio, Aaron Courville. Deep learning. MIT press, 2016