L | Date | Lecturer | Contents | Materials |
---|---|---|---|---|
1 | 25.9. | JK | Introduction, course map, requirements. Linear regression (continuous dependent variable, simple linear regression, p-values). | SAN_intro, SAN_regression, SAN_regression_video |
2 | 2.10. | JK | Multivariate regression (overfitting, model shrinkage). | see the previous slides, SAN_shrinkage_video |
3 | 9.10. | JK | Nonlinear regression (polynomial regression, splines, local regression). | SAN_nlin_regression, SAN_nlin_video |
4 | 16.10. | JK | Nonlinear regression (polynomial regression, splines, local regression). | see the previous slides (and video) |
5 | 23.10. | JK | Discriminant analysis (categorical dependent variable, LDA, logistic regression). | SAN_discriminant, SAN_disc_dimred_video |
6 | 30.10. | JK | Generalized linear models (GLMs). | SAN_GLMs, SAN_GLMs_video |
7 | 6.11. | JK | Dimension reduction (PCA and kernel PCA). | SAN_dimred, SAN_dimred_video |
8 | 13.11. | JK | Dimension reduction (other non-linear methods). | see the previous slides (and video) |
9 | 20.11. | – | Dean's day | no class |
10 | 27.11. | TP | Robust statistics. | SAN_robust, SAN_robust_video |
11 | 4.12. | TP | Anomaly detection. | Anomaly, SAN_anomaly_video |
12 | 11.12. | ZM | Empirical studies, their design and evaluation. Power analysis. | SAN_emp_studies_power_analysis, SAN_emp_studies_video |
13 | 18.12. | JK | Clustering (formalism, k-means, EM GMM, hierarchical). | SAN_clustering, SAN_clustering_video |
14 | 8.1. | JK | Clustering (spectral clustering). | SAN_spect_clustering, SAN_spect_clustering_video |
T | Date | Teacher | Contents | Materials | Previous years |
---|---|---|---|---|---|
1 | 25.9. | JB, AA, JK | Statistical testing, t-test, significance, power of the test. | san_intro.zip | lab1.zip R.pptx |
2 | 2.10. | JB, AA, JK | Simple linear regression. | san_lreg.zip | lab2.zip Statistics and visualization.pptx |
3 | 9.10. | JB, AA, JK | Shrinked linear regression | san_fs.zip | lab3.zip Linear regression.pptx |
4 | 16.10. | JB, AA, JK | Non-linear regression | san_nonlinear.zip, assignment1.zip | lab4.zip Linear_regression_2.pptx demo_exam_question_linreg.pdf |
5 | 23.10. | JB, AA, JK | Discriminant analysis | san_lda.zip, assignment2.zip, reading for assignment 2 | lab5.zip, Linear regression 3.pptx |
6 | 30.10. | JB, AA, JK | Generalized linear models | san_glms.zip, assignment3.zip | lab6.zip assignment2.zip LDA_LR.pptx Elhabian_LDA09.pdf LDA_LR.pdf |
7 | 6.11. | JB, AA, JK | Dimension reduction | san_dimred.zip, assignment4.zip | lab7.zip, GLMs.pptx |
8 | 13.11. | JB, AA, JK | Mid-term test, the final assignment | Final Assignment | |
9 | 20.11. | – | Dean's day | no class | lab8.zip, assignment4.zip Dimensionality reduction.pptx Dimensionality reduction.pdf |
10 | 27.11. | TP | Robust statistics | instruction.pdf | |
11 | 4.12. | TP | Anomaly detection – assignment. | anomaly2.pdf anomaly-problems.zipNotebook in PlutoLink to Pluto | |
12 | 11.12. | ZM | Empirical study design, power analysis | empirical_study, experiment_data_FINAL | |
13 | 18.12. | JB, AA, JK | Clustering | san_clustering.zip | lab12.zip Advanced clustering.pdf Advanced clustering.pptx SAN_solved.pdf |
14 | 8.1. | JB, AA, JK | The final assignment – team presentations | semestral.pdf |
If you are interested and want to learn more, here are some extra resources beyond this course you can look at:
If you have a suggestion on what to add, please let us know. :)