MIT.edu OCW
Designed for 6–7 months of structured, portfolio-driven study (10–12 hrs/week)
| Order | Course | Theme | Duration (weeks) | Application tie-in |
|---|---|---|---|---|
| 0 | 6.S191 – Introduction to Deep Learning | Modern deep-learning foundations (CNNs, RNNs, Transformers) | 4 | TensorFlow/PyTorch hands-on intro; vision, NLP, biology |
| 1 | 6.042J – Mathematics for Computer Science | Discrete math and graphs | 3–4 | SLAM topologies, graph optimization |
| 2 | 6.041SC – Probabilistic Systems Analysis and Applied Probability | Probability and systems | 4–5 | Sensor fusion, Bayesian estimation, uncertainty modeling |
| 3 | 6.036 – Introduction to Machine Learning | Core machine learning | 4–6 | Classification, regression, regularization |
| 4 | 6.801 – Machine Vision | Vision and perception | 4–5 | Mapping, feature detection, optical flow, 3D reconstruction |
| 5 | 6.4210 – Robotic Manipulation | Robotics and control | 5–6 | Motion planning, control loops, state estimation |
| 6 | 9.01 – Introduction to Neuroscience | Biological neural systems | 3–4 | Sensory pathways, motor control, neural coding |
| 7 | 9.13 – The Human Brain | Cognitive neuroscience | 3 | Perception, learning, memory architecture |
| 8 | 9.40 – Introduction to Neural Computation | Computational neuroscience | 4–5 | Modeling neurons and learning rules |
| 9 | 9.66J – Computational Cognitive Science | Cognitive modeling | 4–5 | Probabilistic reasoning, human-like perception |
| 10 | 9.85 – Infant and Adult Cognition | Learning and development | 2–3 | Developmental and reinforcement models |
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