This course is part of the minor track Computational Data Science. It provides an
advanced introduction to
machine learning.
The course is intended for Master's students in physics and mathematics.
Other students are advised to take the
course Statistical machine learning prior to this course.
For physics and math students, this course is the followup of the bachelor course Inleiding Machine Learning
Format: The course will be weekly sessions. Emphasis is on learning the material through mathematical derivation and computer exercises.
Course material:
Week  Topic  Material  Exercises  
1  36  Probability, entropy and inference  MacKay Chapter 2 Further reading: exponential_families.pdf 
Exercises: Mackay 2.10,
extra_opgaven 2.1, 2.2, 2.3, 2.4 Other recommended exercises (not for the grade, solutions are in the book): Mackay 2.14, 2.16ab, 2.18, 2.19, 2.26 
2  37  More about inference Model comparison and Occam's Razor 
MacKay Chapter 3, Chapter 27, Chapter 28 
Exercise MacKay 3.12, 28.1 extra_opgaven 3.1, 27.1, 28.1, 28.2 Other recommended exercises (not for the grade, solutions are in the book): MacKay 3.1, 3.2, 3.5, 3.8, 3.9 
3  38  Classification

See slides Further reading: VC_dimension 
extra_opgaven/Other
exercises/Perceptron 1, 2, 3, 4 
4  39  Gradient methods MLPs 
See slides 
Gradient descent exercise program template MNIST data 
5  40  Deep networks  See slides  Deep learning tensorflow exercise 
6  41  Graphical models  Murphy Chapter 10 sections: 10.1, 10.2.1, 10.2.2, 10.3, 10.4 
extra_opgaven/Other exercises/Graphical models/1 
7  42  Mixture models and EM Autoencoders 
Murphy Chapter 11 sections: 11.1, 11.2 (not 11.2.4), 11.3 (not 11.3.1), 11.4, 11.4.1, 11.4.2 (not 11.4.2.6), 11.4.7 
extra_opgaven/Other
exercises/Mixture models and EM/1,2 Reproduce Murphy fig. 11.11 using the EM algorithm. Here are the data Old Faithful Geyser 
Examination:
There will be no final examination. The students will work in groups of maximum 3 persons. The grade will be based on these exercises.