02 Mathematical Background
Preparation¶
Material¶
Session Description¶
In today's session we look at some of the math behind Machine Learning. Literature: The video "Linear Algebra - Math for Machine Learning". For a very appealing and visual explanation of SVD, you should take a look at Visual Kernel's video on the topic "SVD Visualized".
Key Concepts ¶
- The fundamental role of linear algebra in data representation and computations
- How GPUs harness matrix operations to accelerate machine learning
- Applying advanced techniques like Singular Value Decomposition (SVD)
- Bridging the gap between mathematical concepts and programming implementations
Learning Objectives¶
- Understand why mathematics, particularly linear algebra, calculus, and statistics, is fundamental to machine learning and its optimization processes
- Gain knowledge on how linear algebra facilitates the representation of machine learning data, models, and computations using arrays and matrices.
- Recognize the critical role of GPUs and matrix operations in enhancing the efficiency and capabilities of machine learning algorithms.
- Develop an intuitive grasp of linear algebra by relating it to programming concepts.
- Learn about the application of linear algebra techniques, such as Singular Value Decomposition, in processing and optimizing machine learning data and models.