Every Friday, 10:00
- Introduction
- Lecture
- Practice
- Further Resources
- Video
- English: [autumn 2023]
- українська: [осінь 2020]
- Python
- Lecture
- Practice
- Further Resources
- [WEB SERVICE] Hackerrank (All python)
- [ONLINE TUTORIAL] Python Software Foundation
- [VIDEOLECTURES] An Introduction to Interactive Programming in Python 1, Coursera
- [VIDEOLECTURES] An Introduction to Interactive Programming in Python 2, Coursera
- [WEB SERVICE] CodeSculptor
- [PRESENTATIONS] Workbook from M.Lutz
- [GITHUB REPOSITORY] Design Patterns (advanced topic)
- Video
- English: [autumn 2023]
- українська: [осінь 2020]
- NumPy Arrays
- Lecture
- Practice
- Further Resources
- Video
- English: [autumn 2023]
- українська: [осінь 2020]
- Pandas
- Lecture
- Practice
- Further Resources
- Video
- English: [autumn 2023]
- українська: [осінь 2020]
- MatPlotLib
- Lecture
- Practice
- Further Resources
- Video
- English: [autumn 2023]
- українська: [осінь 2020]
Midterm 1 (Basic Tools): Problem
- Mathematical optimization
- Lecture
- Practice
- Further Resources
- [OFFICIAL DOCUMENTATION] SciPy help
- [ARTICLE] Momentum gradient descent method
- [ARTICLE] Gradient descent methods
- [ARTICLE] Gradient descent methods overview
- [HABR] Newton method
- [ARTICLE] Newton method overview
- [HABR] Gradient descent methods
- [BOOK CHAPTER] MIT, Duality in Linear Programming
- [ARTICLE] Newton's method overview
- [VIDEO] Newton's Fractal
- Video
- English: [autumn 2023]
- українська: [осінь 2020]
- Naive Bayes Classification
- Lecture
- Practice
- Further Resources
- [VIDEO] Veritasium, Bayes
- [ARTICLE] Naive bayes in Python
- [ARTICLE] Spam Filtering with Naive Bayes - Which Naive Bayes?
- [ARTICLE] A Comparison of Event Models for Naive Bayes Text Classification
- [OFFICIAL DOCUMENTATION] Scikit help on Naive Bayes
- [ARTICLE] Understanding Gaussian Classifier
- [VIDEO] Naive Bayes classifier: A friendly approach
- Video
- English: [autumn 2023]
- українська: [осінь 2020]
- Statistics
- Lecture
- Practice
- Further Resources
- Video
- English: [autumn 2023]
- українська: [осінь 2020]
- Linear Regression
- Lecture
- Practice
- Further Resources
- Video
- English: [autumn 2023]
- українська: [осінь 2020]
- Support Vector Machines
- Lecture
- Practice
- Further Resources
- Video
- English: [autumn 2023]
- українська: [осінь 2020]
- Decision Trees and Random Forests
- Lecture
- Practice
- Further Resources
- Video
- English: [autumn 2023]
- українська: [осінь 2020]
Midterm 2 (ML Methods): Problem
- Principal Component Analysis
- Lecture
- Practice
- Further Resources
- [VIDEO] MIT lecture on SVD
- [VIDEO] Numberphile video on PCA
- [HABR] How PCA works
- [ARTICLE] Introduction to PCA
- [PDF] A Tutorial on Principal Component Analysis
- [ARTICLE] PCA and SVD explained with numpy
- [ARTICLE] PCA: Application in Machine Learning
- [VIDEO] University of Waterloo: Principal Component Analysis
- [ARTICLE] Principal Component Analysis Explained Visually
- [ARTICLE] Principal Component Analysis — A Brief Introduction
- Video
- English: [autumn 2023]
- українська: [осінь 2020]
- K-Means Clustering
- Lecture
- Practice
- Further Resources
- Video
- English: [autumn 2023]
- українська: [осінь 2020]
- Gaussian Mixture Models
- Lecture
- Practice
- Further Resources
- Video
- English: [autumn 2023]
- українська: [осінь 2020]
- Kernel Density Estimation
- Lecture
- Practice
- Further Resources
- Video
- English: [autumn 2023]
- українська: [осінь 2020]
- Manifold Learning
- Lecture
- Practice
- Further Resources
- [OFFICIAL DOCUMENTATION] Scikit-learn documentation on Manifold Learning
- [ARTICLE] How to Use t-SNE Effectively
- [WEBSITE] LLE algo, papers and more
- [ARTICLE] Locally Linear Embedding (LLE)
- [VIDEO] Lecture on Locally Linear Embedding from Johns Hopkins University
- [VIDEO] Lecture on from MDS, Isomap, LLE from University of Waterloo
- [VIDEO] Dimensionality Reduction: Why we take Eigenvectors of the Similarity Matrix?
- Video
- English: [autumn 2023]
- українська: [осінь 2020]
- What's next: NNs and beyond
- Lecture
- Practice
- Not planned
- Further Resources
![](https://raw.githubusercontent.com/fbeilstein/machine_learning/master/end.gif)
- Jake Vanderplas, Python Data Science Handbook.
- David Barber, Bayesian Reasoning and Machine Learning.
- Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning.
- Simon J.D. Prince, Computer vision: models, learning and inference.
- C. Bishop, Pattern Recognition and Machine Learning.
- Lutz M., Learning Python
- Jeffrey Elkner, Allen B. Downey, and Chris Meyers, How to Think Like a Computer Scientist: Interactive Edition
- Брэд Миллер и Дэвид Рэнум, Алгоритмы и структуры данных
- Wes McKinney, "Python for Data Analysis" (by the original creator of Pandas)
- Claus O. Wilke, Fundamentals of Data Visualization
- Aurélien Géron, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow
- Darrell Huff, How to Lie With Statistics