EE 541: A Computational Introduction to Deep Learning
Schedule
Week 1
Aug 24
- lecture Lecture 01: Deep Learning Principles and Paradigms
- homework Homework 1
- reading Reading 1
- demo Demos
Week 2
Aug 31
- lecture Lecture 02: Python Fundamentals
- homework Homework 2
- reading Reading 2
No class, Labor Day
Sep 07
- No class, Labor Day
Week 3
Sep 14
- lecture Lecture 03: MMSE Estimation and Prediction
- homework Homework 3
- reading Reading 3
- demo Demos
Week 4
Sep 21
- lecture Lecture 04: Regression, Maximum Likelihood, and Information Theory
- homework Homework 4
- reading Reading 4
Week 5
Sep 28
- lecture Lecture 05: Classification and Logistic Regression
- homework Homework 5
- reading Reading 5
- demo Demos
Week 6
Oct 05
- lecture Lecture 06: Backpropagation
- homework Homework 6
- reading Reading 6
Week 7
Oct 12
- quiz Quiz 1 — Weeks 1–6
Week 8
Oct 19
- lecture Lecture 07: PyTorch: Introduction
- homework Homework 7
- reading Reading 8
Week 9
Oct 26
- lecture Lecture 08: PyTorch: Building MLPs
- homework Homework 8
- reading Reading 9
- demo Demos
Week 10
Nov 02
- lecture Lecture 09: Convolutional Neural Networks (CNN)
- homework Homework 9
- reading Reading 10
- demo Demos
Week 11
Nov 09
- lecture Lecture 10: Convolutional Architectures
- reading Reading 11
Nov 13
- project Project Proposal Due
Week 12
Nov 16
- lecture Lecture 11: PyTorch: Optimizing Training. Data Engineering
- reading Reading 12
Week 13
Nov 23
- lecture Lecture 12: Auto-encoders and Embedding. Recurrent Neural Networks (RNN)
- reading Reading 13
Week 14
Nov 30
- quiz Quiz 2 — Weeks 8–13
Dec 06
- project Project Deliverables Due