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# Lecture Outline
- Basics I: Introduction to DDE ([notes](/Lecture%201/Lecture_1.pdf))
- Basics I: Introduction to DDE ([notes](/DDE_I_ML_Dynamical_Systems/Lecture%201/Lecture_1.pdf))
- Basics II: An Ode to Learning ([notes](/Lecture%202/Lecture_2.pdf)& [code](/Lecture%202/Lecture_0.ipynb))
- Basics II: An Ode to Learning ([notes](/DDE_I_ML_Dynamical_Systems/Lecture%202/Lecture_2.pdf)& [code](/DDE_I_ML_Dynamical_Systems/Lecture%202/Lecture_0.ipynb))
- Analysis of Static Datasets I:
- Classification ([notes](/Lecture%203/Lecture_3.pdf) & [code](/Lecture%203/Lecture_1.ipynb))
- Regression ([notes](/Lecture%204/Lecture_4.pdf) & [code](/Lecture%204/Lecture_2.ipynb))
- Classification ([notes](/DDE_I_ML_Dynamical_Systems/Lecture%203/Lecture_3.pdf) & [code](/DDE_I_ML_Dynamical_Systems/Lecture%203/Lecture_1.ipynb))
- Regression ([notes](/DDE_I_ML_Dynamical_Systems/Lecture%204/Lecture_4.pdf) & [code](/DDE_I_ML_Dynamical_Systems/Lecture%204/Lecture_2.ipynb))
- Analysis of Static Datasets II:
- Clustering ([notes](/Lecture%205/Lecture_5.pdf) & [code](/Lecture%205/Lecture_3.ipynb))
- Dimensionality Reduction ([notes](/Lecture%206/Lecture_6.pdf) & [code](/Lecture%206/Lecture_4.ipynb))
- Clustering ([notes](/DDE_I_ML_Dynamical_Systems/Lecture%205/Lecture_5.pdf) & [code](/DDE_I_ML_Dynamical_Systems/Lecture%205/Lecture_3.ipynb))
- Dimensionality Reduction ([notes](/DDE_I_ML_Dynamical_Systems/Lecture%206/Lecture_6.pdf) & [code](/DDE_I_ML_Dynamical_Systems/Lecture%206/Lecture_4.ipynb))
- Deep Learning for Dynamical Systems ([notes](/Lecture%207/Lecture_7.pdf) & [code](/Lecture%207/Lecture_5.ipynb) & [project template](/Lecture%207/SS2020_Project_Template))
- Deep Learning for Dynamical Systems ([notes](/DDE_I_ML_Dynamical_Systems/Lecture%207/Lecture_7.pdf) & [code](/DDE_I_ML_Dynamical_Systems/Lecture%207/Lecture_5.ipynb) & [project template](/DDE_I_ML_Dynamical_Systems/Lecture%207/SS2020_Project_Template))
- Sequence Modeling I ([notes](/Lecture%208/Lecture_8.pdf) & [code](/Lecture%208/Lecture_6.ipynb)& [code](/Lecture%209/Lecture_6_added.ipynb))
- Sequence Modeling I ([notes](/DDE_I_ML_Dynamical_Systems/Lecture%208/Lecture_8.pdf) & [code](/DDE_I_ML_Dynamical_Systems/Lecture%208/Lecture_6.ipynb) & [code](/DDE_I_ML_Dynamical_Systems/Lecture%209/Lecture_6_added.ipynb))
- Sequence Modeling II ([notes](/Lecture%209/Lecture_9.pdf) & [code](/Lecture%209/Lecture_7.ipynb))
- Sequence Modeling II ([notes](/DDE_I_ML_Dynamical_Systems/Lecture%209/Lecture_9.pdf) & [code](/DDE_I_ML_Dynamical_Systems/Lecture%209/Lecture_7.ipynb))
- Generative Modeling I: Autoencoders ([notes](/Lecture%2010/Lecture_10.pdf) & [code]([code](/Lecture%2010/Lecture_8.ipynb)))
- Generative Modeling I: Autoencoders ([notes](/DDE_I_ML_Dynamical_Systems/Lecture%2010/Lecture_10.pdf) & [code](/DDE_I_ML_Dynamical_Systems/Lecture%2010/Lecture_8.ipynb))
- Generative Modeling II: VAEs & GANs ([notes](/Lecture%2011/Lecture_11.pdf) & [code]([code](/Lecture%2011/Lecture_9.ipynb)))
- Generative Modeling II: VAEs & GANs ([notes](/DDE_I_ML_Dynamical_Systems/Lecture%2011/Lecture_11.pdf) & [code](/DDE_I_ML_Dynamical_Systems/Lecture%2011/Lecture_9.ipynb))
- Machine Learning Control: Introduction ([notes](/Lecture%2012/Lecture_12.pdf) & [code]([code](/Lecture%2012/Lecture_10.ipynb)))
- Machine Learning Control: Introduction ([notes](/DDE_I_ML_Dynamical_Systems/Lecture%2012/Lecture_12.pdf) & [code](/DDE_I_ML_Dynamical_Systems/Lecture%2012/Lecture_10.ipynb))
- Emerging Concepts and the Outlook ([notes](/Lecture%2013/Lecture_13.pdf))
- Emerging Concepts and the Outlook ([notes](/DDE_I_ML_Dynamical_Systems/Lecture%2013/Lecture_13.pdf))
## About the lecture notes and videos
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