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fbeilstein / machine_learning

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Schedule

Every Friday, 9:00

Curriculum

Tools of ML

  1. Introduction
  1. Python
  1. NumPy Arrays
  1. Pandas
  1. MatPlotLib

Midterm 1 (Basic Tools): Problem

Methods of ML

  1. Mathematical optimization
  1. Naive Bayes Classification

  1. Statistics
  1. Linear Regression
  1. Support Vector Machines
  1. Decision Trees and Random Forests

Midterm 2 (ML Methods): Problem

  1. Principal Component Analysis
  1. K-Means Clustering
  1. Gaussian Mixture Models
  1. Kernel Density Estimation
  1. Manifold Learning
  1. What's next: NNs and beyond

For the curious mind

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

Books

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

How it was made

Future plans

Bureaucratic Shenanigans

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