MA 4183/6183: Mathematical Foundations of Machine Learning - Spring 2025
Logistics
- Instructor: Prof. Seongjai Kim
- Classroom: TBA; MW 12:30-1:45PM
- Office Hours: MWF 9:00-9:50AM
- Textbook: Seongjai Kim,
Mathematical Foundations of Machine Learning, Lecture Note.
Machine_Learning_Lecture.pdf
- Please feel free to contact me for any questions, suggestions, or problems.
Course Objectives
We will cover all subjects in the lecture note,
which include
- Ch.1: Introduction to Machine Learning
- Ch.2: Python Basics
- Ch.3: Simple Machine Learning Algorithms for Classification
- Ch.4: Gradient-based Methods for Optimization
- Ch.5: Popular Machine Learning Classifiers
- Ch.6: Data Preprocessing in Machine Learning
- Ch.7: Feature Extraction: Data Compression
- Ch.8: Cluster Analysis
- Ch.9: Neural Networks and Deep Learning
- Ch.10: Data Mining
- Ch.11: Quadratic Programming (when time available)
Prerequisites and Expectations
- MA-2743 (Calculus 4) and MA-3113 (Introduction to Linear Algebra)
- Alternatively, MA-2923 (Introduction to Modern Scientific Computing)
would help you understand machine learning much more easily.
MA-2923 is offered in each Fall semester.
- Additional materials covered in class may appear on exams
and in homework assignments.
- Attendance in class is expected.
See details:
MA4183-6183-Machine-Learning-Syllabus.pdf
Back to Kim's Home Page