Linear Algebra for Machine Learning, Level II: Matrix Tensors (ML Foundations Series)

• Deep Dive into Matrix Tensors: This class offers an in-depth exploration of matrix tensors, expanding on the foundational knowledge from introductory linear algebra and highlighting their significance in all machine learning methodologies.
• Practical Application of Theory: With a balanced mix of theory and interactive examples, students will gain practical skills in applying linear algebra to solve complex problems in high-dimensional spaces, using popular machine learning frameworks.
• Stepping Stone to Advanced Concepts: Serving as a critical link in the Machine Learning Foundations series, this course not only solidifies the learners' understanding of linear algebra but also prepares them for more advanced topics, particularly in Linear Algebra for Machine Learning, Level III: Eigenvectors, enhancing their overall mastery in the field.

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 Level II: Matrix Tensors, builds on the basics of linear algebra. It is essential because matrix properties are central to all machine learning approaches. Through the measured exposition of theory paired with interactive examples, you’ll develop an understanding of how linear algebra is used to solve for unknown values in high-dimensional spaces. The content covered in this class is itself foundational for several other classes in the Machine Learning Foundations series, especially Linear Algebra for Machine Learning, Level III: Eigenvectors.