This is the primary course website for Mathematical Foundations of Deep Neural Networks (심층신경망의 수학적 기초), M1407.001200, Fall 2022. We will also use eTL as a secondary website.
This class will have weekly homework assignments. Submit completed assignments through eTL.
Course material will be posted on this website. eTL will be used for announcements, homework submission, and receiving homework and exam scores.
Ernest K. Ryu, 27-205,
Joo Young Choi
Jae yeon Kim
Hybrid lecture, in-person at 43-1-201 and Zoom, Tuesdays and Thursdays 5:00–6:15pm. We thank the Faculty of Liberal Education (기초교육원) for helping us execute the hybrid lecture. Live (in-person or online) attendance is required.
The midterm and final exams will be hand-written (no computers), in-person, and 4 hours long.
Homework 30%, midterm exam 30%, final exam 40%.
Good knowledge of the following subjects is required.
No prior backround in machine learning or deep learning is required.
Although programming is an essential part of deep learning, I do not want to make substantial Python programming background a prerequisite for this course. Therefore, I will record and upload a Python tutorial for students to study before the start of the semester. I expect this tutorial to be about 5 hours long, and it will cover the necessary Python programming concepts such as variables, loops, functions, classes, objects, inheritance, exceptions, Python lists, Python tuples, Python decorators, Python iterators, the NumPy library, and the Matplotlib library. Of course, students already familiar with these concepts need not spend time with the tutorial.
Programming in deep learning tends to be relatively simple if one utilizes the frameworks such as PyTorch. These frameworks do utilize advanced programming concepts such as inheritance, decorators, and iterators, but if you are roughly familiar with these concepts, I do not expect the Python programming to be a major challenge in this course.
The course does not have a designated reference or a textbook. However, you may find the following references useful: Fei Fei Li's CS 231n at Stanford, Pieter Abbeel's Deep Unsupervised Learning at Berkeley, Dive into Deep Learning by Zhang, Lipton, Li, and Smola, and Deep Learning by Goodfellow, Bengio, and Courville.
Throughout this class, you will spend many hours computing gradients. While the chain rule is taught in any vector calculus curriculum, chain rule formulae in vector and matrix forms are not. Fundamentally, you should first carry out the gradient calculations elementwise and then collect the result into a vectorized form. Nevertheless, you may find the following references useful: Matrix calculus notes 1, matrix calculus notes 2, and matrix calculus notes 3 (Chapters 1–3).