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Office Hours: By appointment via email (PIAI #335; 인공지능연구원 335호)
Overview
Continuous computation, which involves continuous data and operations, is fundamental to many areas of computer science such as machine learning and scientific computing.
Common examples include evaluating mathematical functions (e.g., numpy.exp(x)) and computing derivatives of programs (e.g., jax.grad(f)).
This course introduces the foundations of continuous computation.
It covers a broad range of topics, from basic computations (e.g., evaluating math functions) to advanced ones (e.g., computing derivatives).
Students will learn how to perform these computations correctly and efficiently on actual computers.
In essence, this course serves as a continuous counterpart to an Algorithms course,
a computational counterpart to a Calculus/Probability course, and a foundational counterpart to a Numerical Analysis course.
Lectures will start from first principles and emphasize both mathematical theory and computational methods.
Prerequisites
Students should have a basic knowledge of Algorithms (CSED331), Calculus (MATH101), and Probability (MATH230).
This course is highly math-intensive, so 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), Introduction to Numerical Analysis (MATH351), and Analysis I (MATH311).
Students are welcome to audit this course.
Grades
Attendance (0%): You may earn extra credit if you attend all classes.
Homework (10%): You will have 4~5 homework assignments.
Exam (90%): You will have a midterm (45%) and a final (45%).
Note: The weights of the above items are subject to change.
Digital Devices
All digital devices (laptops, tablets, smartphones, etc.) must not be visible on desks during class.
Exceptions: tablets with styluses (only for note-taking); devices for emergencies or accessibility.