This course is part of the minor track Computational Data Science. It provides an
advanced introduction to
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 follow-up 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.
|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
Further reading: VC_dimension
exercises/Perceptron 1, 2, 3, 4
Gradient descent exercise
|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
|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 220.127.116.11), 11.4.7
exercises/Mixture models and EM/1,2
Reproduce Murphy fig. 11.11 using the EM algorithm. Here are the data Old Faithful Geyser
There will be no final examination. The students will work in groups of maximum 3 persons. The grade will be based on these exercises.