Intro to Probability Theory (ML Foundations Series)

• Essential for ML: Develop a foundational understanding of probability theory, which is crucial for machine learning algorithms that operate in the (uncertain!) real world.
• Theory meets practice: Gain practical knowledge of key concepts such as random events, random variables, probability distributions, and distribution metrics through a blend of theory and interactive examples in Jupyter notebooks.
• Foundational class: Build a strong foundation for further study in the Probability and Statistics subject area, as this class serves as a prerequisite for several other classes in the ML Foundations Series.

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)

• Intro to Linear Algebra
• Linear Algebra II: Matrix Tensors
• Linear Algebra III: Eigenvectors

Calculus (four classes)

• Intro to Calculus
• Calculus II: Automatic Differentiation
• Calculus III: Partial Derivatives
• Calculus IV: Gradients and Integrals

Probability and Statistics (four classes)

• Intro to Probability
• 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.

(Note that at any given time, only a subset of the ML Foundations classes will be scheduled and open for registration.)

This class, Intro to Probability, is a primer on the mathematical field that enables us to make predictions despite uncertainty. Probability theory is essential because machine learning algorithms are both trained by imperfect data and deployed into noisy, real-world scenarios they haven’t encountered before. Through the measured exposition of theory paired with interactive examples, you’ll develop a working understanding of random events, random variables, probability distributions, and metrics for describing distributions. The content covered in this class is itself foundational for several other classes, especially the three remaining classes in the Probability and Statistics subject area.