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开源软件地址(OpenSource Url)：

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开源软件介绍(OpenSource Introduction)：

Schedule

Every Friday, 10:00

Curriculum

Tools of ML

1. Introduction

• Lecture
  • Notes in Colab
• Practice
  • Workbook in Colab
• Further Resources
  • [BLOG] ML in simple words (RU)
  • [BLOG] ML in simple words (EN)
  • [WEB SERVICE] Hackerrank (Minimal requirements)
• Video
  • English: sorry, no video available
  • українська: [осінь 2020]

2. Python

• Lecture
  • Notes in Colab
• Practice
  • Workbook in Colab
• 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: sorry, no video available
  • українська: [осінь 2020]

3. NumPy Arrays

• Lecture
  • Comprehensive Notes in Colab
• Practice
  • Workbook in Colab
• Further Resources
  • [OFFICIAL DOCUMENTATION] NumPy and SciPy
  • [HABR] NumPy
• Video
  • English: sorry, no video available
  • українська: [осінь 2020]

4. Pandas

• Lecture
  • Comprehensive Notes in Colab
• Practice
  • Workbook in Colab
• Further Resources
  • [OFFICIAL DOCUMENTATION] Pandas online documentation
  • [ONLINE TUTORIAL] 10 minutes to Pandas
  • [ONLINE TUTORIAL] cookbook
  • [ONLINE TUTORIAL] SQL Interactive Tutorial (not Pandas but worth mentioning)
• Video
  • English: sorry, no video available
  • українська: [осінь 2020]

5. MatPlotLib

• Lecture
  • Comprehensive Notes in Colab
• Practice
  • Workbook in Colab
• Further Resources
  • [OFFICIAL DOCUMENTATION] Matplotlib online documentation
  • [GALLERY] Matplotlib gallery
  • [ARTICLE] Ten Simple Rules for Better Figures
  • [OFFICIAL DOCUMENTATION] Basemap Documentation
• Video
  • English: sorry, no video available
  • українська: [осінь 2020]

Midterm 1 (Basic Tools): Problem

Methods of ML

1. Mathematical optimization

• Lecture
  • Notes in Colab
• Practice
  • Workbook in Colab
• 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: sorry, no video available
  • українська: [осінь 2020]

2. Naive Bayes Classification

• Lecture
  • Notes in Colab
• Practice
  • Workbook in Colab
• 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: sorry, no video available
  • українська: [осінь 2020]

3. Statistics

• Lecture
  • Notes in Colab
• Practice
  • Workbook in Colab
• Further Resources
  • [HABR] How To Lie With Statistics
  • [ARTICLE] Howard Wainer, How to Display Data Badly, The American Statistician (1984)
• Video
  • English: sorry, no video available
  • українська: [осінь 2020]

4. Linear Regression

• Lecture
  • Notes in Colab
• Practice
  • Workbook in Colab
• Further Resources
  • [HABR] Основы линейной регрессии
  • [VIDEO] Gentle introduction to 1D regression
  • [VIDEO] 1D regression basics
  • [VIDEO] R^2 explained
  • [VIDEO] Multiple regression
  • [VIDEO] 1D regression
  • [LECTURE NOTES] Eberly College of Science lecture notes
  • [INTERACTIVE] Ordinary Least Squares Regression
• Video
  • English: sorry, no video available
  • українська: [осінь 2020]

5. Support Vector Machines

• Lecture
  • Notes in Colab
• Practice
  • Workbook in Colab
• Further Resources
  • [ARTICLE] Sequential Minimal Optimization (SMO) Algorithm
  • [DEMO] Support Vector Machine (SVM) Explorer
  • [PRESENTATION] Support Vector Machines and Kernel Methods: Status and Challenges
  • [HABR] Машина опорных векторов в 30 строчек
• Video
  • English: sorry, no video available
  • українська: [осінь 2020]

6. Decision Trees and Random Forests

• Lecture
  • Notes in Colab
• Practice
  • Workbook in Colab
• Further Resources
  • [VIDEO] Random Forests, StatQuest
  • [VIDEO] AdaBoost, StatQuest
  • [ARTICLE] Feature Selection Using Random forest
  • [NOTES] Random Forests
  • [ARTICLE] On Boosting: Theory and Applications
• Video
  • English: sorry, no video available
  • українська: [осінь 2020]

Midterm 2 (ML Methods): Problem

7. Principal Component Analysis

• Lecture
  • Notes in Colab
• Practice
  • Workbook in Colab
• 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: sorry, no video available
  • українська: [осінь 2020]

8. K-Means Clustering

• Lecture
  • Notes in Colab
• Practice
  • Workbook in Colab
• Further Resources
  • [VIDEO] K-Means overview
  • [VIDEO] Cool K-Means demonstration
  • [VIDEO] K-Means simple intro
  • [VIDEO] K-Means and image segmentation
  • [VIDEO] in depth K-Means with python examples
  • [HABR] K-Means and C-Means
  • [DOCS] sklearn help page
  • [DOCS] Stanford lecture notes
  • [ARTICLE] A Tutorial on Spectral Clustering
• Video
  • English: sorry, no video available
  • українська: [осінь 2020]

9. Gaussian Mixture Models

• Lecture
  • Notes in Colab
• Practice
  • Workbook in Colab
• Further Resources
  • [VIDEO] Madras University lecture on GMM
  • [VIDEO] Madras University lecture on EM
  • [VIDEO] GMM and EM
  • [VIDEO] Stanford lecture on GMM
  • [VIDEO] 1D GMM
  • [VIDEO] Univ. of Washington on Gaussian Mixture Models for Clustering
  • [ARTICLE] Easy intro to GMM
  • [ARTICLE] GMM
• Video
  • English: sorry, no video available
  • українська: [осінь 2020]

10. Kernel Density Estimation

• Lecture
  • Notes in Colab
• Practice
  • Workbook in Colab
• Further Resources
  • [VIDEO] Intro to KDE
  • [ARTICLE] Kernel Density Estimation with Python using Sklearn
  • [ARTICLE] Literature review on KDE & KDE python package
  • [ARTICLE] Kernel Density Estimation in Python
  • [COURSE] A Short Course on Nonparametric Curve Estimation
• Video
  • English: sorry, no video available
  • українська: [осінь 2020]

11. Manifold Learning

• Lecture
  • Notes in Colab
• Practice
  • Workbook in Colab
• 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: sorry, no video available
  • українська: [осінь 2020]

12. What's next: NNs and beyond

• Lecture
  • Presentation (PDF)
• Practice
  • Not planned
• Further Resources
  • [VIDEO LECTURES] CS231n (famous Stanford course)

For the curious mind

1. Applying machine learning to physics
2. Машинное обучение (курс лекций, К.В.Воронцов)

Books

0. Jake Vanderplas, Python Data Science Handbook.
1. David Barber, Bayesian Reasoning and Machine Learning.
2. Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning.
3. Simon J.D. Prince, Computer vision: models, learning and inference.
4. C. Bishop, Pattern Recognition and Machine Learning.
5. Lutz M., Learning Python
6. Jeffrey Elkner, Allen B. Downey, and Chris Meyers, How to Think Like a Computer Scientist: Interactive Edition
7. Брэд Миллер и Дэвид Рэнум, Алгоритмы и структуры данных
8. Wes McKinney, "Python for Data Analysis" (by the original creator of Pandas)
9. Claus O. Wilke, Fundamentals of Data Visualization
10. Aurélien Géron, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow
11. Darrell Huff, How to Lie With Statistics

How it was made

• [GIF] Demo Gallery
• [CODE] Demo Gallery
• Retrospection, tips and tricks for courses creators

Future plans

• Modifications intended for the course restart
• Test Ground (drafts, different tests, and just trash stockpiled here)

Bureaucratic Shenanigans

• Different info to copy-paste into a variety of bureaucratic forms and documents