Intro to Calculus (Machine Learning Foundations)

• Introduction to the Core of Calculus: This class serves as an essential introduction to calculus, focusing on the concepts of limits and derivatives, which form the basis of understanding rates of change—a fundamental aspect in optimizing machine learning algorithms.
• Critical for Machine Learning Optimization: By emphasizing the computation of derivatives through differentiation, the course directly supports the mastery of optimization techniques crucial for machine learning, including backpropagation and stochastic gradient descent used in deep learning.
• Foundational for Advanced Studies: As the gateway to more complex topics in calculus and machine learning, this course lays the groundwork necessary for tackling advanced classes in the Machine Learning Foundations series, particularly Calculus II-IV and the comprehensive class on Optimization, ensuring a robust mathematical foundation for future learning.

The Machine Learning Foundations series of online trainings provides a comprehensive overview of all 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:

1. 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

2. 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

3. Probability and Statistics (four classes)

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

4. 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. To be pushed notifications of upcoming classes in the series, sign up for the instructor’s email newsletter at jonkrohn.com.)

This class, Calculus for Machine Learning: Intro, introduces the mathematical field of calculus — the study of rates of change — from the ground up. It is essential because computing derivatives via differentiation is the basis of optimizing most machine learning algorithms, including those used in deep learning such as backpropagation and stochastic gradient descent. Through the measured exposition of theory paired with interactive examples, you’ll develop a working understanding of how calculus is used to compute limits and differentiate functions. The content covered in this class is itself foundational for several other classes in the Machine Learning Foundations series, especially Calculus II-IV and the final class in the series, on Optimization.