Learning goals:
Natural Language Processing [NLP]- Training software (tensorboard etc.)
TransformersTransfer Learning (Priority)(Mike)- Exploritoritory Data Analysis (EDA) / Efficient Data Preprocessing (Hanieh)
- Containerization (Brianna!!!!!!)
Auto-Encoders (Andy)Genetic Algorithms (Jose)- Heuristic Methods
Model Interpretibility (Mike)- Metrics
Computer VisionPhysics Informed ML (Raman)- Physics Informed ML / PINNs, v2 (Alvin!)
- Adversarial Learning + Generative (GANNS)
Self/Semi-Supervised Learning (Mike)- Online-Learning
- Dimensionality Reduction
- Stocastic Neighbor Embedding
AWS/CLoud Computing (Raman)- Quantum Computing (Brianna)
Self-Organizing Maps (Andy)- Bayesian NN (Opal)
- Graph NN (Jose)
Deep Emulators (Raman)KANNs (Brianna) Kolomogorov born 1903 died in the 80s ANDREEE- Boosting
- Backpropagation (also, talk about NN differentiability!)
Attribution methods & information missingness (Raman)ML Flow (Brianna)- SHAP
- Feature engineering with CCA (Tae)
- Nov 6:
- Nov 13: Integrated Gradients v2 (Raman)
- Nov 20:
- Nov 27:
- Dec 4:
- Dec 11: PINNs (Alvin) (Hopefully)
- Feb 4:
- Feb 18:
- Mar 4:
- Mar 18: Raman
- Apr 1: MLFlow (Brianna...or will she?)
- Apr 15: Mike (defense prequel)
- Apr 29:
- May 13:
- May 27:
Fall 2024 Talk Schedule
- Oct 1:
- Oct 8:
- Oct 15:
- Oct 22:
- Oct 29: Deep Emulators (Raman)
- Nov 5:
- Nov 12: Model Interpretability (Mike) / Entropy &/in ML (Hanieh)
- Nov 19: KANNs (Brianna)
- Nov 26:
- Dec 3:
Spring 2024 Talk Schedule:
- Feb 13: Talha - Variational AutoEncoders
- Feb 27: Self/Semi Supervised Learning (Mike)
- Mar 12: Pytorch - Messi(GOAT) (Tomi)
- Mar 26: Online Learning (Jose)
- Apr 9: Eclipse (Public ML lecture)
- Apr 23: LLMs (Brianna)
- May 7: PINNs (Raman)
- May 21:
