Logistics
Overview
- Continuous computing, **which involve continuous data and operations, is fundamental to diverse areas of computer science, including machine learning and scientific computing. Examples include evaluating continuous functions (e.g., numpy.exp(x)) and computing derivatives (e.g., jax.grad(f)).
- This course introduces the foundations and principles of continuous computing. It covers a broad range of topics: from basic computations (e.g., evaluating elementary functions and generating random numbers), to more advanced computations (e.g., computing derivatives and estimating integrals). Students will learn how to perform these computations correctly and efficiently **on actual computers.
- In essence, this course serves as a continuous counterpart to a traditional algorithm course, and a computational counterpart to a traditional calculus course. Lectures will start from first principles, and emphasize both mathematical theory and computational methods.
Prerequisites
- Students should have a basic knowledge of calculus, probability, and algorithms. As this course is math-intensive, students should be comfortable with understanding and doing rigorous mathematical proofs.
- The following courses are relevant but not required: Automata & Formal Languages (CSED341), Programming Languages (CSED321), Analysis I (MATH311), and Introduction to Numerical Analysis (MATH351). Students are welcome to audit this course.
Grades
- Attendance (0%). Use the electronic attendance check system.
- Homework (50%). You will have 4 homework assignments.
- Project (50%). You will study a list of papers.
- Important Notes.
- Weights. The weights of the above items are subject to change.
- LLMs. You should not ask any homework problem and attach any paragraph from project papers, directly to LLMs (e.g., ChatGPT, Gemini, DeepSeek). However, you could use them to get some help for your study (e.g., asking the concepts covered in the class or presented in project papers).
- Homework. You should not use LLMs when solving homework problems.
- Project. You could use LLMs when doing your project. If you do so, you should clearly mention in the final report how and where LLMs are used.
Links