| 2-INF-150: 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 | |
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Administration. Introduction. Supervised learning / regression. Linear regression and it's variants. Literature: GBC:2.1-2.4 or B:C; GBC:5.1 or B:3.1 or HTF:3.1-3.2 |
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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 | |
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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 |
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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 | |
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Theory of learning cont. Regularization. Tutorials 2: regression |
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lecture: [ linka ] Tutorials 2: Regression: [ linka ] |
stanford (chapter 1): [ linka ] |
| Week 9.-13.10.2023 | |
| Classification. Logistic regression, softmax (maximum entropy) classifier. Neural networks. | |
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lecture: [ linka ] |
stanford (chapter 3, 5 and 9.3): [ linka ] |
| Week 16.-20.10.2022 | |
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Tutorials 3: neural networks Support vector machines. |
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Tutorials 3: neural networks: [ linka ] lecture: [ linka ] |
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| Week 23.-27.10.2023 | |
| Support vector machines (continued). Kernel trick. How to handle inseparable data. | |
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lecture1: [ linka ] lecture2: [ linka ] |
SVM Stanford: [ linka ] |
| Week 31.10.-3.11.2023 | |
| Decision trees. | |
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lecture: [ linka ] |
Decision Trees: [ linka ] Decision Trees 2: [ linka ] |
| Week 6.-10.11.2023 | |
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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. |
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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 | |
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Practical tips for ML. Tutorials 4: trees and SVMs |
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Tutorials 4: trees and SVMs: [ linka ] |
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| Week 20.11-24.11.2023 | |
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Principal component analysis (PCA) Clustering (k-means, k-medoids, hierarchical clustering) |
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Video lecture PCA: [ linka ] Clustering slides: [ PDF, 185 Kb ] Clustering lecture (from minute 50): [ linka ] |
PCA: [ PDF, 567 Kb ] |
| Week 27.11-1.12.2023 | |
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Reinforcement learning Tutorials 5: PCA |
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Reinforcement learning notes (contain more links at the end): [ linka ] Tutorials 5: PCA: [ linka ] |
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| Week 4-8.12.2023 | |
| History of Large Language Models. |
| Week 11-15.12.2023 | |
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How to build an ML product. Final tutorial. |