Repository for Statistical Learning Methods [223490-0286] - Summer semester 2019/20
Links to Team meetings for Class 2 18.03.2020
17:10 - R code
19:00 - Python code
All groups are invited (if you can't join there are additional materials in Class2 folder), meeting is mandatory for groups 104 and 105.
https://forms.gle/5Dxecx3NKxqL6a3CA
Contact
Name: Łukasz Kraiński
Email: lukasz.krainski123@gmail.com
Lecturers
• lecturer: Bogumił Kamiński
• laboratories: Groups 100 and 101 – Michał Kot, Group 102 – Kinga Siuta, Group 103 - Agata Skorupka, Groups 104 and 105 – Łukasz Kraiński
Schedule
• lectures: Tuesdays, 8:00-10:35, Aula IV
• laboratories: room A-113 (day and hour according to group division)
Lectures
| Date | Subject |
|---|---|
| 25-02-20 | Introduction to data science; McKinsey case study |
| 03-03-20 | Working with Git and Github |
| 10-03-20 | Introduction to Julia programming for data science |
| 17-03-20 | Introduction to predictive modeling |
| 24-03-20 | Introduction to threading and distributed computing K-nearest neighbors algorithm |
| 31-03-20 | Methods of evaluation of predictive model quality |
| 07-04-20 | Working with data frames in Julia |
| 21-04-20 | Methods of predictive model selection |
| 28-04-20 | Regularization for predictive modeling |
| 05-05-20 | Introduction to approximation and local predictive models |
| 12-05-20 | Introduction to deep learning |
| 19-05-20 | Causality modeling: introduction |
| 26-05-20 | Causality modeling: algorithms |
| 02-06-20 | Storytelling with data |
| 09-06-20 | Data science in production environments + written examination |
Laboratories
| # | Subject |
|---|---|
| 1 | Refresher on R and Python programming |
| 2 | Methods of evaluation of classifiers |
| 3 | Nonparametric regression models: smoothing spline, LOESS, GAM |
| 4 | Classical machine learning models: CART, random forest |
| 5 | Deep learning |
| 6 | Modeling competition |
| 7 | Computer exam |
Literature
Stephen Boyd and Lieven Vandenberghe, Introduction to Applied Linear Algebra (http://vmls-book.stanford.edu/)
Gareth J., Witten D., Hastie T., Tibshirani R. (2013), An Introduction to Statistical Learning with Applications in R (http://www-bcf.usc.edu/~gareth/ISL/)
Hastie T., Tibshirani R., Friedman J. (2013), The Elements of Statistical Learning (http://www-stat.stanford.edu/~tibs/ElemStatLearn/)
Optional: Kamiński B., Zawisza M. (2012), Receptury w R. Podręcznik dla ekonomisty, Oficyna Wydawnicza SGH (http://bogumilkaminski.pl/projekty/)
Optional: B. Kamiński, P. Szufel: Julia 1.0 Programming Cookbook, Packt Publishing, 2018 (https://www.packtpub.com/application-development/julia-10-programming-cookbook)
Course evaluation criteria
• Written examination (50 points); during last lecture; no supporting materials are allowed
• Laboratory examination (50 points); during last examination; you can bring your own printed materials
• Possible extra points: homeworks, competition
Grading rules
| From | To | Final grade |
|---|---|---|
| 0 | 49 | 2.0 |
| 50 | 59 | 3.0 |
| 60 | 69 | 3.5 |
| 70 | 79 | 4.0 |
| 80 | 89 | 4.5 |
| 90 | 100 | 5.0 |