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.
- 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.
Notice:
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. | Topic | Member | Time |
|---|---|---|---|
| 1 | Recognizing instruments from an audio track | Gero Birkhölzer | 01.28 |
2 | Using Machine Learning for Generating Piano Notes | Adam Norris, Jan-laun Cieslik | 01.28 |
| 3 | Picture generation via audio input | Hilmi Can Yumak, Yannick Bosch, Anton Zickenberg | 01.30 |
| 4 | Predicting NBA Playoffs with Machine Learning | Kerem Dönmez, Deniz Dölek | 01.30 |
| 5 | Using News to Predict Stock Movements | Livia Mano, Egi Papa | 02.04 |
| 6 | Foreign exchange rates prediction using LSTM Recurrent Neural Network | Huy Phung, Tashi Choden, Sahil Pasricha | 02.04 |
| 7 | Dog Breed Identification | Denis Makarov, Timo Jockers | 02.06 |
| 8 | Position prediction for birds | Wilhelm Kerle, Jannis Denecke Nikolas Schwarz | 02.06 |
| 9 | Dog Breed Identification | Julius Rauscher, Julia Klein, Thomas Enderle | 02.11 |
| 10 | Whale Recognition | Joobin Youn, Ying Wang, Ziyao He | 02.11 |
| 11 | Letter localization | Markus Köhler | 02.13 |
| 12 | A recommendation system | Perla Gjoka, Klea Koçi, Tea Jani | 02.13 |
| 13 | Detect the mood of tweets and facebook posts (Quit) | Tolga Tuncer, Valentin Wagner | 02.18 |
| 14 | Apiary health detection | Gemza Ademaj, Souvik Mondal, Gent Ymeri | 02.18 |
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
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