The schedule is subject to change : The course website is still under construction; please check back frequently.
| Date | Lecture | Homework / Readings | Logistics | Module 1: ML and DL Foundations |
|---|---|---|---|
| Week 1 Jan 17 |
1. Course Introduction 2. My Research Overview: AI Robustness and Trustworthiness 3. My Research Overview: AI4Science and ML Systems 4. Framing ML Problems 5. ML as Function Approximation 6. Linear Models 7. Job or Ph.D.? Is it a Question. |
Lecture Note 1 | |
| Week 2 Jan 24 |
1. Project idea discussion 2. Classical ML - Decision Trees and Ensembles; tree of thoughts in LLMs - k-Nearest Neighbors; kNN-LLMs - Clustering (with LLMs) - Anomaly Detection 3. Cloud computing service tutorial |
Lecture Note 2 | |
| Week 3 Jan 31 |
1. Classical ML (continued) Neural Network Basics - Perceptron Revisited - Gradient Descent - Forward Propagation - Activations in LLMs - Finetuning LLMs without backpropagation via hypertuning 2. Project idea discussion |
Lecture Note 3 | |
| Week 4 Feb 7 |
1. Neural Network Basics - Backpropagation - Vanishing Gradient 2. Different types of Neural Networks: - Convolutional Neural Networks Guest Lecture: Nikos Kanakaris (USC) - How to design effective prompts with large models for real-world applications Nikos Kanakaris |
Quiz 1 | Course Project Teams Formed; Pre-proposal DUE; Lecture Note 4 |
| Week 5 Feb 14 |
1. Different types of Neural Networks: - Convolutional Neural Networks 2. Deep Learning Software Tutorial (maybe) |
Assignment 1 OUT | Lecture Note 5 Lecture Note 6 |
| Week 6 Feb 21 |
Different types of Neural Networks: 1. Recurrent Neural Networks (RNN) & LSTM 2. Graph Neural Networks (GNN) Guest Lecture: Maria Shaukat (LinkedIn) - Responsible AI & AI/ML Career Opportunities Maria Shaukat |
Lecture Note 7 | |
| Week 7 Feb 28 |
Automated ML and Transfer Learning LLM fine-tuning |
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| Week 8 Mar 7 |
MIDTERM EXAM (in class, paper-based, open book but no electronics) | ||
| Week 9 Mar 14 |
Training dynamics Generative AI 1. Generative adversarial networks (GAN) 2. Variational AutoEncoder (VAE) 3. Case Study on Controllable Text Generation Guest Discussion (TBD) |
Assignment 2 OUT | Assignment 1 DUE Lecture Note 8 |
| Week 10 Mar 21 |
NO CLASS; Spring Recess | Module 2: Deep Learning Applications & Advanced Topics | |
| Week 11 Mar 28 |
Advanced Topics 1. LLM Decoding 2. Mixture of Experts (MoE) 3. Attention, Relation, and Memory Networks Guest Lecture: Prof. Yang Shi (Utah State) - Deep Learning in Education Prof. Yang Shi's Website |
Quiz 2 | Project Midterm Report DUE Lecture Note 9 |
| Week 12 Apr 4 |
Advanced Topics Contrastive Learning and Self-supervised Learning Guest Lecture: Yongyi (Colin) Zang - Audio Deepfake Detection: Techniques and Ongoing Challenges Yongyi (Colin) Zang's LinkedIn |
Lecture Note 10 Assignment 2 DUE |
|
| Week 13 Apr 11 |
Advanced Topics LLM copyright, Scaling Law Guest Discussion: Prof. Yaoheng Yang and Ruishan Liu |
Lecture Note 11 | |
| Week 14 Apr 18 |
Reinforcement Learning Team Project Presentations (in person) |
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| Week 15 Apr 25 |
Team Project Presentations (in person) | Not all the members need presenting | |
| Week 16 May 2 |
Team Project Presentations (in person) | Not all the members need presenting | |
| Final Report | Final Report Due on May 2nd as well | (No in-class Exam) | Final Project Report DUE on gradescope |