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Mathematical Foundations of Machine Learning [MA4801]

Sommersemester 2018

Prof. Dr. Michael M. Wolf

Lecturer: Prof. Dr. Michael M. Wolf
Assistant: Javier Cuesta
Lectures: Wednesday, 12:15-13:45, LMU Physik Hörsaal (Garching Forschungszentrum) Anmeldung
Exercises: Tuesday, 12:15 - 13:45, LMU Physik Hörsaal (Garching Forschungszentrum)
Friday, 14:15-15:45, room MI 02.08.020




The course will provide an introduction to the mathematical foundations of supervised learning theory, neural networks, support vector machines and kernel methods.
Date Content To read Black board notes
April 11 Intro, Statistical learning theory framework, error decomposition Intro, 1.1 and 1.2 in lecture notes  
April 18 PAC bounds, growth function, VC-dimension and VC-dichotomy 1.3, 1.5 and 1.6 in lecture notes lec2
April 25 VC-dimension of vector spaces, covering and packing numbers 1.6 and 1.9 in lecture notes lec3
May 2 Covering number PAC bound, pseudo and fat-shattering dimension 1.9 and 1.10 in lecture notes lec4
May 9 Pseudo and fat-shattering dimension, uniform stability, on-average stability, regularization 1.10 and 1.11 in lecture notes  
May 16 Stochastic learning algorithms, differential privacy, KL-divergence parts of 1.12 in lecture notes lec6
May 23 Mutual information bound, ensemble methods, Bagging, Ada-Boost parts of 1.12 and 1.14 in lecture notes  
May 30 Gradient boosting, biological and artificial neural nets, Perceptrons, Boolean function representation 2.1, 2.2 and parts of 1.14 and 2.3 in lecture notes  
June 6 Storage capacity of neural networks 2.3 in lecture notes  
June 13 Accuracy bounds for non-linear approximations with continuous parameter dependence, Exponential benefits of depth ..., 2.10 in lecture notes  
June 20 Relations between VC-dimension, computational complexity, approximation capability and depth of neural networks 1.6 in lecture notes, ...  
June 27 (Stochastic) gradient descent, backpropagation, algorithmic differentiation 2.6, 2.7 in lecture notes  
July 4 Convergence of (stochastic) gradient descent 2.8 in lecture notes  
July 11 Saddle points, local minima, overview on kernelized SVMs 2.9, 3 in lecture notes  


Basic knowledge in linear algebra, analysis and probability theory is required as well as some elementary Hilbert space theory.

Date List of major updates for lecture notes
June 1 Sections on Bagging and Gradient Boosting added
June 5 Paragraphs added on general NN models and on storage capacity
June 24 Added lower bound on VCdim for parameterized function classes of bounded computational complexity
July 3 Added section on algorithmic differentiation

Click here for a preliminary version of the lecture notes. They will be updated frequently (hence, think twice before printing).

File Date Content Comments Solution
Exercise class 1 Week 17 - 24 April Basic probability review, Hoeffding's inequality Basic Prob. Review Solution 1
Exercise class 2 Week 24 April - 1 May PAC learning, VC dimension and VC-dichotomy Wording in H2.2 changed Solution 2
Exercise class 3 Week 8 - 15 May Covering numbers, pseudo dimension, and fat-shattering-dimension Typo in H3.3b corrected Solution 3
Exercise class 4 Week 15 - 22 May Algorithmic Stability and Uniform covering number H4.3 a bit simplified and more hints added Solution 4
Exercise class 5 Week 22 - 29 May Stability and generalized bounds   Solution 5
Exercise class 6 Week 29 May - 5 June Relative entropy bounds H6.2c Simplified Solution 6
Exercise class 7 Week 5 June - 12 June Emsemble methods, Ada-Boost and Neural netwoks Typo in H7.2 and Hints added Solution 7
Exercise class 8 Week 12 June - 19 June VC dimension of Neural networks, Approximation by Neural networks Typos in H8.1 and H8.3a corrected Solution 8
Exercise class 9 Week 19 June - 26 June Geometry and complexity of neural networks   Solution 9
Exercise class 10 Week 26 June - 3 July Pseudo-dimension, memorization capacity and approximation of Neural networks   Solution 10
Exercise class 11 Week 3 July - 6 July Algorithmic differentiation, LP and Learning   Solution 11
Exercise class 12 Week 6 July - 13 July Open discussion/Preparation for the Exam    


There are many good books on the topic. Recent examples with a focus on mathematical aspects are: Among the classic books with a focus on mathematical results are: