| 2-INF-150: Machine learning (Strojové učenie) Winter 2018 Prednášky a poznámky |
Contacts | Basic information | Homework assigments | Lectures and handouts | Older versions of this course
Here you can find preliminary schedule for semester. This schedule would be updated after finishing particular week of lectures.
Ďalšie zdroje informácií:
We provide parts of literature and external links for each leacture. Prezentation in these materials might be different than presentation during the lecture. There materials should be treated as supplementary.
| Week 24.-28.9.2018 | |
|
Admnistration. Introduction. Supervised learning. Linear regression.
Basic math (gradients, partial derivatives, matrices). Literature: GBC:2.1-2.4; GBC:5.1 |
|
| Slajdy a poznámky: | Ďalšie materiály: |
|---|---|
|
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 1.-5.10.2018 | |
|
Theory of ML, overfitting, bias, variance.
Tutorials: numpy Literature: GBC:2.1-2.4; GBC:5.1 |
|
| Slajdy a poznámky: | Ďalšie materiály: |
|---|---|
|
Algebra 101: [ PDF, 42 Kb ] Tutorials 1: numpy: [ linka ] |
stanford (chapter 4): [ linka ] stanford (chapter 1): [ linka ] |
| Week 8.-12.10.2018 | |
| Theory of ML, bias variance decomposition. Regularization. Classification. Logistic regression, softmax (maximum entropy) classifier. Probabilistic interpretion of regression. | |
| Slajdy a poznámky: | Ďalšie materiály: |
|---|---|
|
stanford (chapter 3, 5 and 9.3): [ linka ] stanford (chapter 1): [ linka ] |
|
| Week 15.-19.10.2018 | |
|
Neural networks.
Tutorials 2: regression |
|
| Slajdy a poznámky: | Ďalšie materiály: |
|---|---|
|
Tutorials 2: regression: [ linka ] |
|
| Week 22.-26.10.2018 | |
|
Support vector machines.
Tutorials 3: neural networks |
|
| Slajdy a poznámky: | Ďalšie materiály: |
|---|---|
|
Tutorials 3: neural networks: [ linka ] |
Neural nets slides: [ PDF, 11237 Kb ] |
| Week 29.-2.11.2018 | |
| Free week. |
| Week 5.-9.11.2018 | |
| Support vector machines (continued). Decision trees | |
| Slajdy a poznámky: | Ďalšie materiály: |
|---|---|
|
SVM Stanford: [ linka ] Decision Trees: [ linka ] Decision Trees 2: [ linka ] |
|
| Week 12.-16.11.2018 | |
|
Bagging and boosting.
Tutorials 4: trees and SVMs |
|
| Slajdy a poznámky: | Ďalšie materiály: |
|---|---|
|
Tutorials 4: trees and SVMs: [ linka ] |
Gradient boosting: [ linka ] |
| Week 19.-23.11.2018 | |
| 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. | |
| Slajdy a poznámky: | Ďalšie materiály: |
|---|---|
|
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 26.-30.11.2018 | |
| Unsupervised learning Clustering. K-means. Hierarchical clustering. PCA. |
| Week 3.-7.12.2018 | |
|
Leveraging unlabeled data. Feature learning. Semisupervised learning. Active
learning.
Tutorials 5: PCA |
|
| Slajdy a poznámky: | Ďalšie materiály: |
|---|---|
|
PCA: [ PDF, 567 Kb ] Tutorials 5: PCA: [ linka ] |
|
| Week 10.-14.12.2018 | |
| Recommended systems. Reinforcement learning. | |
| Slajdy a poznámky: | Ďalšie materiály: |
|---|---|
|
Stanford notes: [ linka ] Deep Q learning (not exam subject, just for fun): [ linka ] Overview of recommender systems (important stuff is 2.2,2.3,2.4): [ linka ] |
|
| Week 17.-21.12.2018 | |
| Fun with GANs. |