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Machine Learning using Matlab

Lecturer

Dr. Hanhe Lin

Schedule

Lecture Tu. 10:00 - 11:30 M631

Lab Session

Th.13:30 - 15:00Z613
Th.15:15 - 16:45Z613

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

     

Course works and credits

In the course you are required to complete a small project alone or with other classmates. You could choose a project which is related to your study. Or you can choose some interesting topics. For example, face recognition/detection, object classification, etc. 

Credit will be given based on two parts: a presentation (30%) and a technical report (70%, including source code). The presentation should include motivation, data collection, design of the framework. The technical report should include the framework you implement, analysis of experimental results. 

Presentation schedule

Each group has 20 minutes for presentation (15 minutes talk, 5 minutes answer questions). Please bring your student ID

 

NumberPresenterDateTimeLocation
1Wenting Wang, Nida Cilasun, Mete Can Akar

11.07.2017

10:00-10:20M631
2Imant Daunhawer11.07.201710:25-10:45M631
3Andreas Teller, Cullen Boldt11.07.201710:50-11:10M631
4Timo Spinde, Henri Hose11.07.201711:15-11:35M631
5Oliver Wiedemann, Jonathan Hassler18.07.201710:00-10:20M631
6Aleksander Parelo18.07.201710:25-10:45M631
7Larissa Zimmermann, Taehee Kim18.07.201710:50-11:10M631
8Benjamin Wilhelm18.07.201711:15-11:35M631
9Sibora Xhema, Laman Abdullayeva,  Bruno Dhima20.07.201713:30-13:50Z613
10Ziyaddin Ovchiyev, Silas Halle Njita20.07.201713:30-13:50Z613
11Sahib Nasirli, Philipp Meschenmoser, Mehaboobia Parveen Shaik20.07.201713:55-14:15Z613
12Daniel Ohrenhofer20.07.201714:20-14:40Z613
13Maximilian Ortwein20.07.201714:20-14:40Z613

 

 

 

 

Lecture Slides

DateSlides
Lecture 125.04.20171 2 3
Lecture 202.05.2017slide&code
Lecture 309.05.2017slide&code
Lecture 416.05.2017slide
Lecture 523.05.2017slide
Lecture 630.05.2017slide&code
Lecture 706.06.2017slide&code
Lecture 813.06.2017slide
Lecture 920.06.2017slide&code
Lecture 1027.06.2017slide&demo
Lecture 1107.04.2017slide

Lab session documents

DateDocuments
Lab session 127.04.2017slide&code
Lab session 204.05.2017ex1&answer
Lab session 311.05.2017ex2&answer
Lab session 418.05.2017ex3&answer
Lab session 501.06.2017ex4&answer
Lab session 608.06.2017quiz
Lab session 722.06.2017ex5&answer
Lab session 829.06.2017ex6&answer

Matlab Access

University of Konstanz is a member of the state-wide Matlab agreement. University staff as well as students may use the software including all tool boxes for non-commercial, academic research and education. The software may be used on university workstations as well as on private computers. More information can be found on State-wide Matlab agreement page.

Literature / Weblinks

  • Alpaydin, Ethem. Introduction to machine learning. MIT press, 2014.
  • Christopher M.. Bishop. Pattern recognition and machine learning. Springer, 2006.
  • Stanford University CS229 Machine Learning.