Introduction to Algorithms for Data Science and Physics
Course Description This course offers an introduction to the fundamental concepts of algorithms and several widely used methods in machine learning, state estimation/inference, and modern data science. A key objective is to help students understand the underlying principles and theoretical foundations of machine learning algorithms. Topics covered include basic probability and statistical techniques, simulation methods, optimization strategies, and essential ideas from high-dimensional problems and reconstruction algorithms. To keep pace with the rapid evolution of big data and modern analytics, the course also explores a selection of contemporary algorithms developed for large-scale data processing and high-dimensional statistical analysis.
Target Audience This course is intended for advanced undergraduate and graduate students in physics, computer science, data science, or related fields who are interested in the foundations and practical applications of machine learning and modern data analysis. It is also suitable for researchers and professionals seeking to strengthen their understanding of algorithmic methods in state estimation, optimization, computer vision and physics. A basic background in calculus, linear algebra, and probability is recommended, though key concepts will be reviewed as needed throughout the course. You are all very welcome to attend the course!
Exercises and Problems Each class will last approximately 45x3=135 minutes. There will be two types of exercises: analytical problems (such as estimations or derivations) and programming assignments. The analytical problems are relatively straightforward, while the programming tasks require more thoughtful design and implementation. Each week, a few programming/theoretical exercises will be assigned to reinforce the key concepts covered in class. These exercises are designed to closely reflect practical techniques in areas such as physics, state estimation, geometric reasoning under uncertainty, and optimization.
Course Grading Policy
- Homework: 30%, except Module F.
- Quiz: 30%, except Module F.
- Final Exam: 40%, except Module F.
Lecture Notes
Homework
- Assignment 1: before or on 3/10/2026