2INF150: Strojové učenie / Machine Learning Fall 2023 Handouts 
Contact  Basic information  Homework assigments  Handouts  Previous semesters
This page shows preliminary schedule of classes and servers as a repository of materials relevant to class. The schedule will be update regularly after lectures.
Recommended literature:
In the schedule, we list the chapters most relevant to the material covered in class. Presentation of the material in lectures usually differs from the books. The book chapters should serve mainly as an additional materials for self study.
Additional materials:
Week 18.22.9.2023  
Administration. Introduction. Supervised learning / regression. Linear regression and it's variants. Literature: GBC:2.12.4 or B:C; GBC:5.1 or B:3.1 or HTF:3.13.2 

Slides and notes:  Supporting materials: 

video intro: [ linka ] video regression: [ linka ] slides 1: [ PDF, 275 Kb ] slides 2: [ PDF, 342 Kb ] 
stanford (chapters 1, 2, 4): [ linka ] 
Week 25.9.29.9.2022  
Theory of learning, overfit, underfit, bias variance. Tutorials 1: TBD Literature: GBC:4.3,5.9; B:3.1; Tutorials 1: collabora/jupyter notebooks, numpy 

Slides and notes:  Supporting materials: 

Tutorials 1: numpy: [ linka ] Learning theory notes: [ PDF, 362 Kb ] video lecture: [ linka ] 
stanford (chapter 4): [ linka ] stanford (chapter 1): [ linka ] 
Week 2.6.10.2023  
Theory of learning cont. Regularization. Tutorials 2: regression 

Slides and notes:  Supporting materials: 

lecture: [ linka ] Tutorials 2: Regression: [ linka ] 
stanford (chapter 1): [ linka ] 
Week 9.13.10.2023  
Classification. Logistic regression, softmax (maximum entropy) classifier. Neural networks.  
Slides and notes:  Supporting materials: 

lecture: [ linka ] 
stanford (chapter 3, 5 and 9.3): [ linka ] 
Week 16.20.10.2022  
Tutorials 3: neural networks Support vector machines. 

Slides and notes:  Supporting materials: 

Tutorials 3: neural networks: [ linka ] lecture: [ linka ] 
Week 23.27.10.2023  
Support vector machines (continued). Kernel trick. How to handle inseparable data.  
Slides and notes:  Supporting materials: 

lecture1: [ linka ] lecture2: [ linka ] 
SVM Stanford: [ linka ] 
Week 31.10.3.11.2023  
Decision trees.  
Slides and notes:  Supporting materials: 

lecture: [ linka ] 
Decision Trees: [ linka ] Decision Trees 2: [ linka ] 
Week 6.10.11.2023  
bagging and boosting. Theory of ML (PAC learning). Estimating needed number of training samples and expected test error for finite hyphothesis set. Estimation for infinite set of hyphotheses. VC dimension. PAC learning and SVM. 

Slides and notes:  Supporting materials: 

lecture: [ linka ] PAC learning rectangle game: [ linka ] PAC learing, VC dimension (until 50 minute): [ linka ] 
gradient boosting: [ linka ] PAC learning rectangle game: [ linka ] PAC learing, VC dimension (until 50 minute): [ linka ] 
Week 13.17.11.2023  
Practical tips for ML. Tutorials 4: trees and SVMs 

Slides and notes:  Supporting materials: 

Tutorials 4: trees and SVMs: [ linka ] 
Week 20.1124.11.2023  
Principal component analysis (PCA) Clustering (kmeans, kmedoids, hierarchical clustering) 

Slides and notes:  Supporting materials: 

Video lecture PCA: [ linka ] Clustering slides: [ PDF, 185 Kb ] Clustering lecture (from minute 50): [ linka ] 
PCA: [ PDF, 567 Kb ] 
Week 27.111.12.2023  
Reinforcement learning Tutorials 5: PCA 

Slides and notes:  Supporting materials: 

Reinforcement learning notes (contain more links at the end): [ linka ] Tutorials 5: PCA: [ linka ] 
Week 48.12.2023  
History of Large Language Models. 
Week 1115.12.2023  
How to build an ML product. Final tutorial. 