CPTS 434/534 – Neural Network Design and Application
Course Overview
CPTS 434/534 introduces the foundations and practical aspects of deep neural networks, a cornerstone of modern machine learning. The course blends theoretical concepts in statistical learning with hands-on implementation of key deep learning models.
Learning Outcomes:
- Principles of representation learning and optimization
- Implementation of a variety of neural network architectures (MLPs, CNNs, RNNs, transformers, etc.)
- Practical training techniques and modern neural network variants
By the end of the course, students will understand how deep models are designed, trained, and applied to real-world data across vision, sequence and structured data domains.
Syllabus and Schedule
Lecture Schedule
| Lecture | Topic | Slides |
|---|---|---|
| 1 | [0] Syllabus and Overview | |
| 2 | [ML] ML Basics | Slides |
| 3 | [ML] PAC Learning | Slides |
| 4 | [ML] ERM and MLE | Slides |
| 5 | [TA] HW1 and Coding Tools | Slides |
| 6 | [NN] NN Basics | Slides |
| 7 | [NN] Compositing Units in NN | Slides |
| 8 | [NN] MLP Softmax Classifier | Slides |
| 9 | [CNN] Convolutional Layer and CNN | Slides |
| 10 | [CNN] CNN Architectures | Slides |
| 11 | [CNN] Practical Training of CNN | Slides |
| 12 | [RNN] Sequence Data and RNN | Slides |
| 13 | [RNN] LSTM and RNN Architectures | Slides |
| 14 | [ModernNN] Attention Mechanism | |
| 15 | [ModernNN] Transformer and ViT | |
| 16 | [GNN] Graph Data and Representation | |
| 17 | [GNN] GNN | |
| 18 | [GenAI] GAN and VAE | |
| 19 | [GenAI] Diffusion Models | |
| 20 | [FM] Foundation Models | |
| 21 | [FM] Multimodal Foundation Models | |
| 22 | [FL] Federated Learning | |
| 23 | [UQ] Conformal Prediction and Mean-Risk Model | |
| 24 | [Agent] Agentic Workflow |
Assignment Materials
| Assignments | Topic/Section | Materials |
|---|---|---|
| HW1 | ML | Start Code, HW Note |
| HW2 | NN | Start Code |
| HW3 | CNN | Start Code |
| HW4 | Transformer + ViT | |
| HW5 | RNN + GNN | |
| HW6 | GenAI |