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 # Day 1: statistical modeling and shallow ML
+### Lecture text
+[A3NetSS_Uemura.pdf](https://drive.google.com/file/d/1cykvsVTjBs2YK1CBGTsU7BFYm1yp1vhJ/view?usp=sharing)
+### Hands-on exercises (python scripts on Google Colaboratory)
+Copy them to your drive, try running them, and edit as you like.
+
+[01_Bayes.ipynb](https://colab.research.google.com/drive/1bGvg8pP3cG4mbOIKc0XsmBbpy7omNWng?usp=sharing)
+
+[02_LASSO.ipynb](https://colab.research.google.com/drive/1eZckNqCyE6E4Fd5pD0PeWeTwz16LGZwy?usp=sharing)
+
+[03_GaussianProcess.ipynb](https://colab.research.google.com/drive/1fC5vucqdGjzuau5qM69rYaOjdxVC7SUV?usp=sharing)
+
+[04_SVM.ipynb](https://colab.research.google.com/drive/1G1QO205XCASIXIVl3friXppwoGgDnkAO?usp=sharing)