Course Details
16:198:501 - Mathematical Foundations of Data Science
- Course Number: 16:198:501
- Course Type: Graduate
- Semester 1: Fall
- Semester 2: Spring
- Credits: 3
- Description:
Focus on the use of linear algebra and statistical conceptual tools in machine learning and data mining practice. Topics include Matrix Factorizations, Bayesian approaches to Hypothesis testing - Parameter Estimation, Kernels, Density Estimation, Gradient Descent, and Neural Networks.
- M.S. Course Category: Algorithms & Complexity
- Category: A (M.S.)
- Prerequisite Information:
198:205 (Discrete Math), 640:152 (Calculus II), 960:580 (Basic Probability) or equivalents
- Expected Work: Approximately 5 Homework assignments, 3 small group projects, Midterm and Final exams
