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 and Transformer | Slides |
| 15 | [ModernNN] Vision Transformer | Slides |
| 16 | [GNN] Graph Neural Networks | Slides |
| 17 | [GNN] GNN Variants and Oversmoothing | Slides |
| 18 | [GenAI] AR and VAE | Slides |
| 19 | [GenAI] Diffusion Models | Slides |
| 20 | [FM] Foundation Models 1 | Slides adapted from UvA Foundation Models course by Prof. Cees Snoek |
| 21 | [FM] Foundation Models 2 | Slides adapted from UvA Foundation Models course by Prof. Cees Snoek |
| 22 | [FM] Foundation Models 3 | Slides adapted from UvA Foundation Models course by Prof. Cees Snoek |
| 23 | [UQ] Conformal Prediction | |
| 24 | [Agent] Agentic Workflow and Course Summary |
Assignment Materials
| Assignments | Topic/Section | Materials |
|---|---|---|
| HW1 | ML | Start Code, HW Note |
| HW2 | NN | Start Code |
| HW3 | CNN | Start Code |
| HW4 | LSTM+Transformer | Start Code |
| HW5 | Gen AI - VAE & Diffusion Models | Start Code |
| HW6 | TBA |