MIT 18.06: Linear Algebra
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MIT’s 18.06 Linear Algebra is a foundational course that explores the fundamental concepts of vectors, matrices, determinants, eigenvalues, and linear transformations. Linear algebra is one of the most essential mathematical tools in AI.
Why this course?
All operation in the world of AI is based on linear algebra. This course is a must for anyone who wants to understand the math behind AI.
- Core Foundation for Machine Learning and Deep Learning
- Machine learning models rely on matrix operations for training and prediction.
- Neural networks use weight matrices and activations that require linear transformations.
- Gradient-based optimization techniques such as gradient descent involve matrix calculus.
- Machine learning models rely on matrix operations for training and prediction.
- Essential for Dimensionality Reduction and Feature Engineering
- Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) are used in data compression and feature extraction.
- Latent Semantic Analysis (LSA) in Natural Language Processing (NLP) relies on matrix factorization.
- Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) are used in data compression and feature extraction.
- Optimization and AI Model Training
- Least Squares Regression and ridge regression are direct applications of orthogonal projections in vector spaces.
- Matrix calculus is necessary for computing gradients and optimizing model performance.
- Least Squares Regression and ridge regression are direct applications of orthogonal projections in vector spaces.