Table of Contents

Lectures and Tutorials

Lectures

L Date Lecturer Contents Materials
1 23.9. JK Introduction, course map, requirements. Linear regression (continuous dependent variable, simple linear regression, p-values). SAN_intro, SAN_regression, SAN_lecture_1
2 30.9. JK Multivariate regression (overfitting, model shrinkage). see the previous slides, SAN_lecture_2
3 7.10. JK Nonlinear regression (polynomial regression, splines, local regression). SAN_nlin_regression, SAN_lecture_3
4 14.10. JK Nonlinear regression (polynomial regression, splines, local regression). see the previous slides, SAN_lecture_4
5 21.10. JK Discriminant analysis (categorical dependent variable, LDA, logistic regression). SAN_discriminant, SAN_lecture_5
6 28.10. – National holiday no class
7 4.11. JK Generalized linear models (GLMs). SAN_GLMs, SAN_lecture_7
8 11.11. JK Dimension reduction (PCA and kernel PCA). SAN_dimred, SAN_lecture_8
9 18.11. JK Dimension reduction (other non-linear methods). see the previous slides, SAN_lecture_9
10 25.11. TP Robust statistics. lecture notes, slides, SAN_lecture_10
11 2.12. TP Anomaly detection. SAN_anomaly, SAN_lecture_11
12 9.12. ZM Empirical studies, their design and evaluation. Power analysis. SAN_emp_studies_power_analysis, SAN_lecture_12
13 16.12. JK Clustering (formalism, k-means, EM GMM, hierarchical). SAN_clustering, SAN_lecture_13
14 6.1. JK Clustering (spectral clustering). SAN_spect_clustering, SAN_lecture_14

Tutorials

T Date Teacher Contents Materials Previous years
1 23.9. JB, AA, JK Statistical testing, t-test, significance, power of the test. san_intro.zip, r_setup.zip, pres-1.pdf lab1.zip R.pptx
2 30.9. JB, AA, JK Simple linear regression. san_lreg.zip, pres_2.pdf lab2.zip Statistics and visualization.pptx
3 7.10. JB, AA, JK Shrinked linear regression san_fs.zip, assignment1.zip, pres_3.pdf lab3.zip Linear regression.pptx
4 14.10. JB, AA, JK Non-linear regression san_nonlinear.zip, pres_4.pdf lab4.zip Linear_regression_2.pptx demo_exam_question_linreg.pdf
5 21.10. JB, AA, JK Discriminant analysis san_lda.zip, assignment2.zip, reading for assignment 2, pres_5.pdf lab5.zip, Linear regression 3.pptx
6 28.10. – National holiday, dean's day no class lab8.zip, assignment4.zip
7 4.11. JB, AA, JK Generalized linear models san_glms.zip, assignment3.zip, pres_7.pdf lab6.zip assignment2.zip LDA_LR.pptx Elhabian_LDA09.pdf LDA_LR.pdf
8 11.11. JB, AA, JK Mid-term test, the final assignment Final Assignment Dimensionality reduction.pptx Dimensionality reduction.pdf
9 18.11. JB, AA, JK Dimension reduction san_dimred.zip, assignment4.zip, pres_8.pdf, assig4_R_problem.pdf lab7.zip, GLMs.pptx
10 25.11. TP Robust statistics instruction.pdf instruction.pdf
11 2.12. TP Anomaly detection – assignment. anomaly2.pdf anomaly-problems.zipNotebook in PlutoLink to Pluto
12 9.12. ZM Empirical study design, power analysis empirical_study, experiment_data_FINAL
13 16.12. JB, AA, JK Clustering san_clustering.zip, pres_13.pdf, pres_13.odp lab12.zip Advanced clustering.pdf Advanced clustering.pptx SAN_solved.pdf
14 6.1. JB, AA, JK The final assignment – team presentations semestral.pdf

Lecture recordings

Playlist from 2024

Extra Resources for the Curious

If you are interested and want to learn more, here are some extra resources beyond this course you can look at:

  1. Wasserstein, Ronald L., and Nicole A. Lazar. “The ASA statement on p-values: context, process, and purpose.” The American Statistician 70.2 (2016): 129-133. DOI: 10.1080/00031305.2016.1154108

If you have a suggestion on what to add, please let us know. :)