MAT 2215 Mathematics for Machine Learning & Artificial Intelligence
The four pillars of machine learning are regression, dimensionality reduction, density estimation, and classification. This course aims to establish the mathematical foundation upon which to build these pillars by covering carefully selected topics in linear algebra, analytic geometry, vector calculus, probability and distributions, and optimization.
Prerequisites: MAT 1570 or MAT 1580
- Determine norms, inner products, lengths and distances, angles and orthogonality.
- Solve systems of linear equations, compute matrix/vector operations, find determinant and trace of a matrix, find eigenvalues and eigenvectors.
- Differentiate univariate and multivariate functions; find gradients of vector-valued functions and matrices. Linearize a multivariate function and find a multivariate Taylor series. Solve optimization problems using gradient descent and Lagrange multipliers.
- Construct a probability space for discrete and continuous distributions; find probability using various rules and Bayes' Theorem, find summary statistics of a random variable and distribution. Graph, analyze, and interpret Gaussian and other types of distributions.
Credit Hours: 5
- Classroom: 5 hours
- Division: Science, Mathematics and Engineering
- Department: Mathematics
- Repeatable Credit: No
- Offered Online: No
1:00PM to 3:15PM