Course Recommendations

Advanced Robotics

Core grad-level robotics course covering manipulation, planning, and control — directly relevant to most EECS robotics labs

Introduction to Robotics

Foundational undergrad course: kinematics, dynamics, ROS, motion planning — required background for any robotics lab

Robotic Manipulation and Interaction

Covers grasping, contact dynamics, and manipulation — essential for labs like AUTOLab and ROAR

Advanced Control Systems

Deep dive into modern control theory used across robotics and autonomous systems labs

Deep Reinforcement Learning

Sergey Levine's flagship course — directly maps to RAIL Lab and RLL research

Advanced Topics in RL

Special topics course covering cutting-edge RL research, often taught by RL faculty

Introduction to Machine Learning

Foundational ML course — prerequisite understanding for any RL lab

Computer Vision

Core grad-level vision course covering image understanding, 3D reconstruction, and neural rendering

Computational Photography

Hands-on project course with image processing and generative models — great portfolio builder for vision labs

Deep Neural Networks

Covers architectures used in modern vision systems: CNNs, transformers, diffusion models

Natural Language Processing

Covers transformer architectures, language modeling, and LLM fundamentals

Deep Neural Networks

Architecture deep dives: attention, transformers, scaling — the building blocks of foundation models

Large Language Models

Seminar on LLM training, fine-tuning, and deployment — directly relevant to foundation model labs

Safety and Alignment of AI Systems

Stuart Russell's course on value alignment and CIRL — directly maps to CHAI research

Computer Security

Understanding attack surfaces and adversarial thinking — foundational for AI security research

Theoretical Statistics

Statistical foundations useful for formal safety guarantees and robustness proofs

Statistical Learning Theory

Core theory course covering generalization, VC dimension, and statistical learning — essential for theory labs

Theoretical Statistics

Rigorous statistics foundation for anyone doing theoretical ML research

Convex Optimization

Optimization theory underlying most ML algorithms — Ben Recht and Moritz Hardt's wheelhouse

Introduction to Machine Learning

Strong applied ML foundations with mathematical rigor — good starting point before theory courses

Machine Learning Systems

Ion Stoica's course on building ML infrastructure — directly relevant to Sky Lab research

Operating Systems

Systems fundamentals: concurrency, scheduling, distributed systems — essential background for ML systems work

Database Systems

Data management fundamentals relevant to large-scale ML data pipelines and model serving