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)

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 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

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