Applied Linear Algebra in AI and ML

University of California, San Diego via Swayam

Overview

Title: Applied Linear Algebra in AI and ML

Description: Delve into the critical mathematical underpinnings of artificial intelligence (AI) and machine learning (ML) with our specialized course, offered by the University of California, San Diego, through Swayam. Designed to equip senior undergraduate and postgraduate students majoring in Computer Science and Engineering, Electrical Engineering, Electronics and Communication Engineering, AI, and Mathematics with foundational and advanced knowledge, this course lays emphasis on linear algebra, optimization techniques, and statistical methods. These tools are indispensable for the development and understanding of algorithms in AI and ML.

The course aims to introduce and deepen understanding of key concepts and computational techniques in linear algebra that are directly applicable to AI and ML algorithms. What sets this course apart is its focused approach on illustrating how these mathematical concepts are applied to solve real-world problems in AI and ML.

Throughout the course, participants will engage with a variety of topics including but not limited to least squares solution, parameter estimation, cost function analysis, constrained and multi-objective least squares, portfolio optimization, sparse solutions, dictionary learning, eigenvalue eigenvector decomposition, spectral theorem, SVD, the multicollinearity problem, PCA, dimensionality reduction, Google's page ranking algorithm, Markov chains, low rank approximation, SLRA, image de-blurring techniques, tensors, CP tensor decomposition, deep network learning applications, matrix completion, and collaborative filtering techniques.

Intended Audience: This course is tailored for senior undergraduate and postgraduate students from the fields of CSE, EE, ECE, AI, and Mathematics seeking to solidify their understanding of linear algebra in the context of its application in AI and ML.

Prerequisites: A basic course in Engineering Mathematics with some exposure to linear algebra is required to enroll in this program.

Categories: Artificial Intelligence Courses, Machine Learning Courses, Linear Algebra Courses