CSCE 5218 – Deep Learning

Spring 2026    

Basic information:

Course description

This course aims at covering the basics of modern deep neural networks. In specific, the first part will introduce the fundamental concepts in neural networks including network architecture, activation function, loss, optimization, etc. Then, the second part will describe specific types of different deep neural networks such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) and attention-based Transformer, as well as their applications in computer vision and natural language processing. In the final part we will briefly discuss some recent advanced topics in deep learning including graph neural networks, unsupervised representation learning, deep reinforcement learning, generative adversarial networks (GANs), etc. In this course, the hands-on practice of implementing deep learning algorithms (in Python) will be provided via homeworks and course project.

Textbooks

We will have required readings from the following textbook:

Besides, the following textbooks are useful as additional references: In addition to the textbooks, extra reading materials will be provided as we cover topics. Check out the course website regularly for updated reading materials.

Announcements

Links

Paper review list

Important: Read the requirements (click here) for paper review. Here is a review example for your reference. (Paper review lists will be gradually added.)

Paper review list 1 (due on 2/17):

  1. A Krizhevsky, I Sutskever, and G Hinton, ImageNet Classification with Deep Convolutional Neural Networks, NeurIPS, 2012.
  2. A Paszke, et al., PyTorch: An Imperative Style, High-Performance Deep Learning Library, NeurIPS, 2019.

Schedule and class notes (being updated)

Date Lecture Reading Note
Week 1
1/13 		Introduction - -
1/15 Machine learning overview Deep Learning Ch 1-5 -
Week 2
1/20 		Machine learning overview (cont) Deep Learning Ch 1-5 -
1/22 Neural network basics-1 Deep Learning Ch 4.2, 4.3, 6 -
Week 3
1/27 		No Class (due to winter storm) - -
1/29 PyTorch Tutorial - -
Week 4
2/3 		Neural network basics-2 Deep Learning Ch 4.2, 4.3, 6 -
2/5 Deep neural network training-1 Deep Learning Ch 7, 8, 11 -
Week 5
2/10 		Deep neural network training-2 Deep Learning Ch 7, 8, 11 -
2/12 Deep neural network training-3 Deep Learning Ch 7, 8, 11 -
Week 6
2/17 		Deep neural network training-3 (cont.) Deep Learning Ch 7, 8, 11 -
2/18 Convolutional Neural Networks (CNNs)
  • Convolution and Pooling
Deep Learning Ch 9 -