Introduction

This course is designed to lecture theory and applications of modern deep learning. After taking the course, we expect students to be able to understand deep neural network modules for their task of interest.

Textbooks and references

• (Textbook) (DL) Deep Learning (Ian Goodfellow, Yoshua Bengio, Aaron Courville, 2016) [Link]
• (D2L) Dive into Deep Learning (Aston Zhang et al., 2019) [Link]
• (CB) Pattern Recognition and Machine Learning, (Christopher Bishop, 2006) [Link]
• (TM) Machine Learning, (Tom Mitchell, 1997) [Link]
• (DHS) Pattern Classification 2nd Edition, (Richard O Duda, Peter E. Hart, David G. Stork, 2000) [Link]
• (GT) Mathematics for Machine Learning, (Garrett Thomas, 2018) [Link]
• (KM) Machine Learning: a probabilistic perspective, (Kevin Murphy, 2013) [Link]
• (SB) Reinforcement Learning, 2nd Edition, (Richard S. Sutton and Andrew G. Barto, 2018) [Link]

Schedule

Course Policies

Cheating: Any assignment or exam that is handed in must be your own work. However, talking with one another to understand the material better is strongly encouraged. Recognizing the distinction between cheating and cooperation is very important. If you copy someone else's solution, you are cheating. If you let someone else copy your solution, you are cheating. If someone dictates a solution to you, you are cheating. Everything you hand in must be in your own words, and based on your own understanding of the solution. If someone helps you understand the problem during a high-level discussion, you are not cheating. We strongly encourage students to help one another understand the material presented in class, in the book, and general issues relevant to the assignments. When taking an exam, you must work independently. Any collaboration during an exam will be considered cheating. Any student who is caught cheating will be given an D in the course. Please don't take that chance - if you're having trouble understanding the material, please let us know and we will be more than happy to help.