courses:b4m36san:content [CourseWare Wiki]

L	Date	Lecturer	Contents	Materials
1	22.9.	JK	Introduction, course map, requirements. Linear regression (continuous dependent variable, simple linear regression, p-values).	SAN_intro, SAN_regression, SAN_lecture_1_czech, SAN_lecture_1_english
2	29.9.	JK	Multivariate regression (overfitting, model shrinkage).	see the previous slides, SAN_lecture_2_czech, SAN_lecture_2_english
3	6.10.	JK	Nonlinear regression (polynomial regression, splines, local regression).	SAN_nlin_regression, SAN_lecture_3_czech, SAN_lecture_3_english
4	13.10.	JK	Nonlinear regression (polynomial regression, splines, local regression).	see the previous slides, SAN_lecture_4
5	20.10.	JK	Discriminant analysis (categorical dependent variable, LDA, logistic regression).	SAN_discriminant, SAN_lecture_5
6	27.10.	JK	Generalized linear models (GLMs).	SAN_GLMs, SAN_lecture_7
7	3.11.	JK	Dimension reduction (PCA and kernel PCA).	SAN_dimred, SAN_lecture_8
8	10.11.	JK	Dimension reduction (other non-linear methods).	see the previous slides, SAN_lecture_9
9	17.11.	–	National holiday	no class
10	24.11.	TP	Robust statistics.	lecture notes, slides, SAN_lecture_10
11	1.12.	TP	Anomaly detection.	SAN_anomaly, SAN_lecture_11
12	8.12.	ZM	Empirical studies, their design and evaluation. Power analysis.	SAN_emp_studies_power_analysis, SAN_lecture_12
13	15.12.	JK	Clustering (formalism, k-means, EM GMM, hierarchical).	SAN_clustering, SAN_lecture_13
14	5.1.	JK	Clustering (spectral clustering).	SAN_spect_clustering, SAN_lecture_14

T	Date	Teacher	Contents	Materials
1	22.9.	JB, AA, JK	Statistical testing, t-test, significance, power of the test.	san_intro.zip, r_setup.zip, pres-1.pdf
2	29.9.	JB, AA, JK	Simple linear regression.	san_lreg.zip, R cheat sheet, pres_2.pdf
3	6.10.	JB, AA, JK	Shrinked linear regression	san_fs.zip, assignment1.zip, pres_3.pdf
4	13.10.	JB, AA, JK	Non-linear regression	san_nonlinear.zip, pres_4.pdf
5	20.10.	JB, AA, JK	Discriminant analysis	san_lda.zip, assignment2.zip, reading for assignment 2, pres_5.pdf
6	27.10.	JB, AA, JK	Generalized linear models	san_glms.zip, assignment3.zip, pres_7.pdf
7	3.11.	JB, AA, JK	Mid-term test, the final assignment	Final Assignment
8	10.11.	JB, AA, JK	Dimension reduction	san_dimred.zip, assignment4.zip, pres_8.pdf, assig4_R_problem.pdf
9	17.11.	–	National holiday, dean's day	no class
10	24.11.	TP	Robust statistics	instruction.pdf
11	1.12.	TP	Anomaly detection – assignment	anomaly2.pdf anomaly-problems.zip Notebook in Pluto Link to Pluto
12	8.12.	ZM	Empirical study design, power analysis	empirical_study, experiment_data_FINAL
13	15.12.	JB, AA, JK	Clustering	san_clustering.zip, pres_13.pdf, pres_13.odp
14	5.1.	JB, AA, JK	The final assignment – team presentations

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. :)