This is the public course website for Mathematical algorithms II (수학적 알고리즘 2), M1407.000500, Fall 2022. We will use eTL as a secondary website.
This course will cover the three separate topics listed below. Each Friday's class will consist of two 75-minute lectures, and each topic will have roughly eight lectures. The two lectures given on the same day will be on different topics, which necessarily means that the presentation of the topics will not be contiguous.
Mathematical rigor will not be a priority for this course. For neural differential equations and diffusion models, we will focus on calculations and derivations while skipping over technical details. For deep reinforcement learning and natural language processing, we should be able to properly derive the key mathematical results.
Neural differential equations and diffusion models:
Deep reinforcement learning:
Natural language processing and transformers:
Ernest K. Ryu, 27-205,
Fridays 2:00–5:50pm at 24-101. Lectures will be mostly based on slides. In-person attendance is required.
This class will have a final exam. (No midterm.) This exam is open-book in the sense that you may use any non-electronic resource.
Attendance 30%, Homework 30%, and final exam 40%.