courses:b4m36san:content [CourseWare Wiki]

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.zip Notebook in Pluto Link 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:

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. :)