Probability Level II: Distributions and Information Theory (ML Foundations Series)

• ML-focused distributions: Develop a practical understanding of the most common probability distributions used in machine learning through a combination of theory and interactive examples in Python.
• Information theory: Learn to apply information theory to quantify the amount of meaningful signal within a given dataset.
• Real-world ready: Real-world problems are seldom deterministic; learn the foundations for building AI systems that reason well despite this inherent uncertainty.

The Machine Learning Foundations series of online trainings provides a comprehensive overview of all of the subjects — mathematics, statistics, and computer science — that underlie contemporary machine learning techniques, including deep learning and other artificial intelligence approaches. Extensive curriculum detail can be found at the course’s GitHub repo.

All of the classes in the ML Foundations series bring theory to life through the combination of vivid full-color illustrations, straightforward Python examples within hands-on Jupyter notebook demos, and comprehension exercises with fully-worked solutions.

The focus is on providing you with a practical, functional understanding of the content covered. Context will be given for each topic, highlighting its relevance to machine learning. You will be better positioned to understand cutting-edge machine learning papers and you will be provided with resources for digging even deeper into topics that pique your curiosity.

There are 14 classes in the series, organized into four subject areas:

Linear Algebra (three classes)

• Linear Algebra for Machine Learning: Intro
• Linear Algebra for Machine Learning, Level II: Matrix Tensors
• Linear Algebra for Machine Learning, Level III: Eigenvectors

Calculus (four classes)

• Calculus for Machine Learning: Intro
• Calculus for Machine Learning, Level II: Automatic Differentiation
• Calculus for Machine Learning, Level III: Partial Derivatives
• Calculus for Machine Learning, Level IV: Gradients & Integrals

Probability and Statistics (four classes) 

• Intro to Probability Theory
• Probability II and Information Theory
• Intro to Statistics
• Statistics II: Regression and Bayesian

Computer Science (three classes)

• Intro to Data Structures and Algorithms
• DSA II: Hashing, Trees, and Graphs
• Optimization

Each of the four subject areas are fairly independent, however theory within a given subject area generally builds over the 3-4 classes — topics in later classes of a given subject area often assume an understanding of topics from earlier classes. Work through the individual classes based on your particular interests or your existing familiarity with the material.

This class, Probability II and Information Theory, builds directly on the content covered in Intro to Probability Theory. Through the measured exposition of theory paired with interactive examples, you’ll develop a working understanding of the most common probability distributions in machine learning. You’ll also learn how to use information theory to measure how much meaningful signal there is within some given data. The content covered in this class is itself foundational for several other classes, especially the two remaining classes in the Probability and Statistics subject area.