$$ % Define your custom commands here \newcommand{\bmat}[1]{\begin{bmatrix}#1\end{bmatrix}} \newcommand{\E}{\mathbb{E}} \newcommand{\P}{\mathbb{P}} \newcommand{\S}{\mathbb{S}} \newcommand{\R}{\mathbb{R}} \newcommand{\S}{\mathbb{S}} \newcommand{\norm}[2]{\|{#1}\|_{{}_{#2}}} \newcommand{\pd}[2]{\frac{\partial #1}{\partial #2}} \newcommand{\pdd}[2]{\frac{\partial^2 #1}{\partial #2^2}} \newcommand{\vectornorm}[1]{\left|\left|#1\right|\right|} \newcommand{\abs}[1]{\left|{#1}\right|} \newcommand{\mbf}[1]{\mathbf{#1}} \newcommand{\mc}[1]{\mathcal{#1}} \newcommand{\bm}[1]{\boldsymbol{#1}} \newcommand{\nicefrac}[2]{{}^{#1}\!/_{\!#2}} \newcommand{\argmin}{\operatorname*{arg\,min}} \newcommand{\argmax}{\operatorname*{arg\,max}} \newcommand{\dd}{\operatorname{d}\!} $$

Deep Learning

A graduate course on optimization, neural network architectures, generative models, and reinforcement learning. The notes combine theory with computational examples.

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A graduate course on optimization, neural network architectures, generative models, and reinforcement learning. The notes combine theory with computational examples.

Course materials

Foundations

• 1. Basics of Optimization — Notes
• 2. Introduction to Learning — Notes
• 3. Fundamentals of Deep Neural Networks — Notes
• 4. Recognizing Handwritten Digits — Notes
• 5. Measuring and Improving Performance — Notes

Neural architectures

• 6. Convolutional Neural Networks (CNNs) — Notes
• 7. Residual Networks — Notes
• 8. Neural Operators and U-Net — Notes
• 9. Transformers — Notes
• 10. Graph Neural Networks — Notes

Generative models

• 11. Unsupervised Learning — Notes
• 12. Variational Autoencoders — Notes
• 13. Diffusion and Flow Models — Notes

Reinforcement learning

• 14. Reinforcement Learning — Notes

Syllabus and references

• SYSE 6v80 · Deep Learning · Spring 2026 (PDF)