Statistical Machine Learning
Lectures are Monday and Wednesday 10:15-11:30 in Mondi 2 (except Oct 22: big seminar room, office building west)
Recitations are Wednesdays, 11:45-12:30 in Mondi 2
Instructors: Christoph Lampert
Teaching Assistant: Nikola Konstantinov,   Mary Phuong,   Amelie Royer

announcements schedule references

Announcements

Schedule (tentative, all dates are estimates)

Date Lecture Topic Notes Assignments
Oct 08 Mon 1 - A Practical Introduction PDF (updated 15/10/18) handout: exercise sheet 1: PDF
data: wine-train.txt, wine-test.txt
Oct 10 Wed self-study (Christoph traveling) e.g. refresh probability: PDF  
Oct 15 Mon 2 - Bayesian Decision Theory,
Generative Probabilitistic Models
PDF (new) handout: exercise sheet 2: PDF  
Oct 17 Wed 3 - Discriminative Probabilistic Models,
Maximum Margin Classifiers
 
Oct 22 Mon 4 - Optimization, Kernel Classifiers handout: exercise sheet 3 
Oct 24 Wed 5 - More about kernels and optimization; Model Selection
Oct 29 Mon tbd (Christoph traveling)  
Oct 31 Wed 6 - Model Selection; Beyond Binary Classification  
Nov 05 Mon 7 - Learning Theory I handout: exercise sheet 4
Nov 07 Wed 8 - Learning Theory II  
Nov 12 Mon 9 - Structured Prediction I handout: final project
Nov 14 Wed 10 - Structured Prediction II  
Nov 19 Mon 11 - Representation Learning / Deep Learning
Nov 21 Wed 12 - Unsupervised Learning  

References

[1] Christopher Bishop: Pattern Recognition and Machine Learning, Springer, 2007.
[2] Mohri, Rostamizadeh, Talwalkar: Foundations of Machine Learning, MIT Press, 2012.
[3] Shalev-Shwartz, Ben-David: Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014.
[4] Hal Daume III: A Course in Machine Learning, online.
[5] Hal Daume III: Mathematics for Machine Learning, online.
[6] ... and many others

announcements schedule references