Intro to Linear Algebra (Machine Learning Foundations)

• Foundational for Machine Learning: This class serves as a critical foundation for understanding machine learning, as it covers the essential principles of linear algebra, which is at the core of all ML algorithms.
• Interactive Learning Experience: The class offers a unique interactive experience by combining theoretical knowledge with practical examples, allowing students to manipulate tensors — the key elements of linear algebra — in popular frameworks like PyTorch, TensorFlow, and NumPy.
• Gateway to Advanced Topics: As the introductory course in the ML Foundations series, it paves the way for more advanced studies, particularly in Linear Algebra for Machine Learning, Level II: Matrix Tensor and Linear Algebra for Machine Learning, Level III: Eigenvectors, ensuring a comprehensive understanding necessary for mastering the field of machine learning.

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. (https://github.com/jonkrohn/ML-foundations) 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.

This class, Linear Algebra for Machine Learning: Intro, is the first in the Machine Learning Foundations series. It is essential because linear algebra lies at the heart of all machine learning approaches. Through the measured exposition of theory paired with interactive examples, you’ll develop an understanding of what tensors — the fundamental building blocks of linear algebra — are, as well as how to manipulate them in PyTorch, TensorFlow, and NumPy. The content covered in this class is itself foundational for all the other classes in the Machine Learning Foundations series and it is especially relevant to Linear Algebra for Machine Learning, Level II: Matrix Tensor and Linear Algebra for Machine Learning, Level III: Eigenvectors.