Statistical Machine Learning
Lectures are Tuesday and Thursday 8:45-10:00
Recitations time is Thursday 10:00-11:00(new!)
Instructors: Christoph Lampert
Teaching Assistant: Alex Zimin

announcements schedule references

Announcements

Schedule (tentative)

Date Lecture Topic Notes Assignments
Mar 01 Tue 1 - A Practical Introduction PDF sheet: PDF
data: wine-train.txt, wine-test.txt
Mar 03 Thu 2 - Bayesian Decision Theory,
Generative Probabilitistic Models
PDF (new)  
Mar 08 Tue 3 - Discriminative Probabilistic Models,
Maximum Margin Classifiers
PDF (new) sheet: PDF 
Mar 10 Thu 4 - Optimization, Kernel Classifiers PDF (new)  
Mar 15 Tue 5 - More about kernels and optimization; Model Selection PDF (new)
Mar 17 Thu 6 - Model Selection; Beyond Binary Classification PDF (new) sheet: PDF 
Mar 21
-- Apr 01
spring break  
Apr 05 Tue 7 - Learning Theory I slides: PDF
notes: PDF
exercise: PDF
data: census.txt
Apr 07 Thu 8 - Learning Theory II slides: PDF
notes: PDF
 
Apr 12 Tue 9 - Structured Prediction I slides: PDF
Apr 14 Thu 10 - Structured Prediction II slides: see PDF for part I  
Apr 19 Tue 11 - Representation Learning / Deep Learning slides: PDF exercise: sheet 5 (final project)
Apr 21 Thu 12 - Unsupervised Learning slides: PDF  
until May 01 final project    

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] ... and others

announcements schedule references