OMSCS 74
- RL M1: Intro to Reinforcement Learning
- HPC M1: Locality
- ML M1: Introduction
- GIOS M1: Introduction to Operating Systems
- DL M1: Linear Classifiers and Gradient Descent
- NLP M1 + M2: Intro and Foundations
- RL S1: Intro to Reinforcement Learning
- ML M2: Decision Trees
- GIOS M2: Processes
- DL M2: Neural Networks
- NLP M3: Classification
- RL M2: RL Basics
- ML M3: Regression
- GIOS M3: Threads and Concurrency
- DL M3: Optimization of Deep Neural Networks
- NLP M4: Language Modeling
- RL S2: Markov Decision Processes
- ML M4: Neural Networks
- GIOS M4: PThreads
- DL M4: Data Wrangling (Meta)
- NLP M5: Semantics
- RL M3: TD and Friends
- ML M5: Instance-Based Learning
- GIOS M5: Thread Design
- DL M5: Convolutional and Pooling Layers
- NLP M6: Modern Neural Architectures
- ML M7: Kernel Methods and SVMs
- ML M6: Ensemble Learning
- GIOS M6: Thread Performance
- DL M6: CNN Backprop + Common Architectures
- NLP M7: Information Retrieval (Meta)
- ML M8: Computational Learning Theory
- GIOS M7: Scheduling
- DL M7: CNN Visualization
- NLP M8: Task-Oriented Dialogue (Meta)
- GIOS M8: Memory Management
- DL M8: Advanced Computer Vision Architectures
- NLP M9: Applications Summarization (Meta)
- ML M9: VC Dimension
- GIOS M9: Inter-Process Communication
- DL M9: Introduction to Structured Representations
- ML M10: Bayesian Learning
- GIOS M10: Synchronization Constructs
- NLP M10: Machine Reading
- DL M10: Language Modeling (Meta)
- ML M11: Randomized Optimization
- ML M11: Bayesian Inference
- GIOS M11: IO Management
- NLP M11: Open Domain Question Answering (Meta)
- DL M11: Neural Attention Models (Meta)
- ML M12: Clustering
- GIOS M12: Virtualization
- NLP M12: Machine Translation
- DL M12: Machine Translation (Meta)
- ML M13: Feature Selection
- GIOS M13: Remote Procedure Calls
- NLP M13: Private AI (Meta)
- DL M13: Generative Modeling
- ML M14: Feature Extraction
- GIOS M14: Distributed File Systems
- NLP M14: Responsible AI
- DL M13.1: Denoising Diffusion Probabilistic Models
- ML M15: Information Theory
- GIOS M15: Distributed Shared Memory
- DL M14: Embeddings (Meta)
- ML M16: Markov Decision Processes
- GIOS M16: Datacenter Technologies
- DL M15: Scalable Training
- ML M17: Reinforcement Learning
- DL M16: Responsible AI
- ML M18: Game Theory (Part 1)
- ML M19: Game Theory (Part 2)
- DL M18: Unsupervised and Semi-Supervised Learning
- DL M19: Translation and ASR (Meta)