| 2-INF-150: Strojové učenie / Machine Learning Fall 2020 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:
Schedule (pointers to video lectures will come later):
| Week 21.-25.9.2020 | |
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Admnistration. Introduction. Supervised learning. Linear regression.
Basic math (gradients, partial derivatives, matrices). Literature: GBC:2.1-2.4; GBC:5.1 |
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| Slides and notes: | Supporting materials: |
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slides 1: [ PDF, 135 Kb ] slides 1b: [ PDF, 225 Kb ] slides 2: [ PDF, 158 Kb ] slides 2(english): [ PDF, 157 Kb ] slides 3a: [ PDF, 252 Kb ] slides 3a(english): [ PDF, 250 Kb ] |
stanford (chapters 1, 2, 4): [ linka ] |
| Week 28.9.-2.10.2020 | |
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Theory of ML, overfitting, bias variance decomposition.
Tutorials: numpy Literature: GBC:2.1-2.4; GBC:5.1 |
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| Slides and notes: | Supporting materials: |
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Algebra 101: [ PDF, 42 Kb ] Tutorials 1: numpy: [ linka ] |
stanford (chapter 4): [ linka ] stanford (chapter 1): [ linka ] |
| Week 5.-9.10.2020 | |
| Regularization. Classification. Logistic regression, softmax (maximum entropy) classifier. Probabilistic interpretion of regression. | |
| Slides and notes: | Supporting materials: |
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meeting link: [ linka ] lecture notes: [ PDF, 139 Kb ] lecture: [ linka ] lecture 2 (until 35 minute, stop at neural networks): [ linka ] |
stanford (chapter 3, 5 and 9.3): [ linka ] stanford (chapter 1): [ linka ] |
| Week 12.-16.10.2020 | |
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Neural networks.
Tutorials 2: regression |
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| Slides and notes: | Supporting materials: |
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lecture: [ linka ] Tutorials 2: Regression: [ linka ] |
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| Week 19.-23.10.2020 | |
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Support vector machines.
Tutorials 3: neural networks |
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| Slides and notes: | Supporting materials: |
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Tutorials 3: neural networks: [ linka ] Notes: [ linka ] |
Neural nets slides: [ PDF, 11237 Kb ] |
| Week 26.-30.10.2020 | |
| Support vector machines (continued). Kernel trick. | |
| Slides and notes: | Supporting materials: |
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lecture1: [ linka ] lecture2: [ linka ] |
SVM Stanford: [ linka ] |
| Week 2.-6.11.2020 | |
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Decision trees. Bagging and boosting.
Tutorials 4: trees and SVMs |
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| Slides and notes: | Supporting materials: |
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lecture: [ linka ] Tutorials 4: trees and SVMs: [ linka ] |
Decision Trees: [ linka ] Decision Trees 2: [ linka ] Gradient boosting: [ linka ] |
| Week 9.-13.11.2020 | |
| 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: |
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Lecture notes: [ linka ] PAC learning rectangle game: [ linka ] PAC learing, VC dimension (until 50 minute): [ linka ] |
PAC - finite hyphotheses: [ PDF, 243 Kb ] axis aligned rectangles / Andres Munoz, NY Univ: [ PDF, 97 Kb ] VC dimension - definition and examples / Yishai Mansour, U Tel Aviv: [ PDF, 109 Kb ] VC dimension - PAC estimates / Yishai Mansour, U Tel Aviv: [ PDF, 173 Kb ] PAC estimates for SVM: [ PDF, 295 Kb ] |
| Week 23.-27.11.2020 | |
| Supervised learning review. | |
| Slides and notes: | Supporting materials: |
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Notes: [ linka ] |
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| Week 23.-27.11.2020 | |
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Unsupervised learning Tuesday: Watch intro into unsupervised learning. Wednesday: Lecture about clustering |
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| Slides and notes: | Supporting materials: |
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Lecture (from minute 50): [ linka ] Notes: [ linka ] |
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| Week 30.-4.12.2020 | |
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Tuesday: Watch lecture about PCA (and optionally about nonlinear methods).
Wednesday: Tutorials
Tutorials 5: PCA |
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| Slides and notes: | Supporting materials: |
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Video lecture PCA: [ linka ] Nonlinear PCA (optional): [ linka ] Tutorials 5: PCA: [ linka ] |
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| Week 7.-11.12.2020 | |
| Tuesday: Semisupervised learning. Wednesday: Reinforcement learning. | |
| Slides and notes: | Supporting materials: |
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Tuesday notes: [ linka ] Reinforcement learning notes: [ linka ] |
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| Week 14.-18.12.2020 | |
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Tuesday: Recommender systems Wednesday: Current ML highlights |