Machine Learning 1 (Winter Term 2025/2026)
Overview
    - Course consisting of:
    
        - Lectures (2 hours/week) in TRE/PHYS/E (Zellescher Weg 16) on Fridays, 09:20–10:50
        
- Exercise group sessions (2 hours/week)
            
                - BAR/0218/U, Tuesdays, 16:40–18:10
                
- APB/E001/U, Thursdays, 16:40–18:10 
                
                
- APB/E001/U, Fridays, 14:50–16:20 
                
- APB/E001/U, Fridays, 16:40–18:10 
                
- Online, Thursdays, 16:40–18:10 
            
 
- Self-study
        
- Final examination
    
 
- Enrolment (OPAL). Additional rules for enrolment may apply, depending on the study program.    
Contents
- Introduction
- Supervised learning
- Introduction
- Learning of binary decision trees
- Hardness
- Local search algorithm 
 
- Learning of linear functions
- Logistic regression
- Convexity
- Gradient descent algorithm
 
- Learning of composite functions (deep learning)
- Compute graphs
- Non-convexity
- Forward-propagation algorithm and backward-propagation algorithm
- Attention
- Transformers
 
 
- Semi-supervised and unsupervised learning
- Introduction
- Partitioning (clustering)
- Clique partition problem
- Hardness
- Local search algorithms
 
- Ordering
- Linear ordering problem
- Hardness
- Local search algorithms
 
- Classifying
- Multi-label classification problem
 
 
- Supervised structured learning
- Introduction
- Conditional graphical models
- Message passing algorithms
- Pseudo-Boolean optimization
 
- Linear and integer optimization for machine learning
- Introduction
- Simplex algorithm
- Branch-and-bound/branch-and-cut algorithms
 
Textbooks