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Lectures and Tutorials


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

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. :)

courses/b4m36san/content.txt · Last modified: 2024/01/09 10:43 by klema