Date Lecture (and links to advance videos when applicable) Links and
Notes		 Reading Suggestions Comments
Intro and Basics 1/20 Introduction and Logistics Intro & logistics    
1/22 Edge AI and Mitigating Resource Bottlenecks
 Bottlenecks    
1/27 Overfitting and Self-supervised Learning   Group requests due
1/29 Edge AI and Mitigating the Data Bottleneck   All groups assigned
The Data Bottleneck: Self- Supervised Data- Efficient Learning for IoT 2/3 Class Project Ideas Introduction.      
2/5 Fundamentals of Self-Supervised Learning: Tokenization, Pre-training, Fine-tuning, Backbone Architectures (e.g., auto-encoders, transformers, etc), and Issues with Scaling Laws for IoT Applications     Project Title, Abstract, and Member List Due
2/10 RNNs, LSTMs, and State Space Models     Project Title and Abstract due
2/12 Representation Learning from Multimodal Sensor Data (Instructor) HW1 Out    
2/17 Representation Learning from Multimodal Sensor Data (Student Led)  
HW1 Debate + Student led talk
2/19 Self-supervised Learning from Frequency Domain Data (Instructor)
 HW2 Out    
2/24 Self-supervised Learning from Frequency Domain Data (Student Led)  
HW2 Debate + Student led talk
2/26 Handling Spatial-Temporal IoT Data (Instructor) HW3 Out 2-page project proposal due
3/3 Handling Spatial-Temporal IoT Data (Student Led)  
HW3 Debate + Student led talk;
Data Curation and "Faking" 3/5 Physical Data Curation and Augmentation (Instructor) HW4 Out    
3/10 Physical Data Curation and Augmentation (Student Led)
HW4 Debate + Student led talk
3/12 Project Elevator Talks      
Break 3/17  Spring Break
3/19
The Compute Bottleneck: Efficient Inference at the IoT Edge 3/24 Input Data Filtering  
Instructor intro [+ student led talk]
3/26 Model Reduction: Pruning, Quantization, Distillation HW5 Out   Instructor intro [+  student led talk]
3/31 Neural Network Architecture Search     Instructor intro + HW5 Debate [+ student led talk]
4/2 Mixture of Experts Cascades HW6 Out   Instructor intro [+ student led talk]
4/7 Timing Guarantees   Instructor intro + HW6 Debate [+ student led talk]
4/9 Energy Consumption and Thermal Issues HW7 Out   Instructor intro + HW7 Debate [+ student led talk]
4/14 Federated Learning, Distributed Fine-Tuning, and Test-Time Adaptation     Instructor intro [+ student led talk]
4/16 Closed loop control and related foundation models (RT-2, RT-X, etc) HW8 Out Instructor intro [+ student led]
Ethics 4/21 Ethical and Societal Considerations HW8 Debate
4/23  
Student Projects 4/28 Student-led Final Project Presentations      
4/30 Student-led Final Project Presentations      
5/5 Recap