Mathématiques pour la Science des Données

Je rassemble ici des références pour alimenter mon cours de mathématiques appliquées du M1 BME Mathematics.

C’est un cours introductif pour un public essentiellement de biologistes avec des bases mathématiques, disons… anciennes. Cours qui combine pour l’instant combine: un peu d’algèbre linéaire, rappels d’analyse, introduction aux équations différentielles.

Mathematics for Data Science

Ibrahim Sharaf ElDen

resources to 3 sections (Linear Algebra, Calculus, Statistics and probability), the list of resources will be in no particular order, resources are diversified between video tutorials, books, blogs, and online courses.

Linear Algebra

Used in machine learning (& deep learning) to understand how algorithms work under the hood. It’s all about vector/matrix/tensor operations; no black magic is involved!

  1. Khan Academy Linear Algebra series (beginner-friendly).
  2. Coding the Matrix course (and book).
  3. 3Blue1Brown Linear Algebra series.
  4. fast.ai Linear Algebra for coders course, highly related to modern ML workflow.
  5. The first course in Coursera Mathematics for Machine Learning specialization.
  6. “Introduction to Applied Linear Algebra — Vectors, Matrices, and Least Squares” book.
  7. MIT Linear Algebra course, highly comprehensive.
  8. Stanford CS229 Linear Algebra review.

Calculus

Used in machine learning (&deep learning) to formulate the functions used to train algorithms to reach their objective, known by loss/cost/objective functions.

  1. Khan Academy Calculus series (beginner-friendly).
  2. 3Blue1Brown Calculus series.
  3. The second course in Coursera Mathematics for Machine Learning specialization.
  4. The Matrix Calculus You Need For Deep Learning paper.
  5. MIT Single Variable Calculus.
  6. MIT Multivariable Calculus.
  7. Stanford CS224n Differential Calculus review.

Statistics and Probability

Used in data science to analyze and visualize data, to discover (infer) helpful insights.

  1. Khan Academy Statistics and probability series (beginner-friendly).
  2. A visual introduction to probability and statistics, Seeing Theory.
  3. Intro to Descriptive Statistics from Udacity.
  4. Intro to Inferential Statistics from Udacity.
  5. Statistics with R Specialization from Coursera.
  6. Stanford CS229 Probability Theory review.

Bonus materials

  1. Part one of Deep Learning book.
  2. CMU Math Background for ML course.
  3. The Math of Intelligence playlist by Siraj Raval.

So, that was me giving away my carefully curated Math bookmarks folder for the common good! Hope that helps you expand your machine learning knowledge, and fight your fear of discovering what’s happening behind the scenes of your sklearn/Keras/pandas import statements.

Your contributions are very welcomed, through reviewing one of the listed resources or adding new awesome ones.

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