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fa24/interml/calendar.md

@@ -47,7 +47,7 @@ Week | Dates |  Topics | Demos & Tutorials |  Lecture Slides | Readings & Notes
 3 | Sep 9, 11 | Density estimation and Mercer kernels |  [<img width="25" src="colab.svg">](https://colab.research.google.com/github/YData123/sds365-fa24/blob/master/demos/smoothing/smoothing-demo3.ipynb) [Density estimation demo](https://github.com/YData123/sds365-fa22/raw/main/demos/smoothing/smoothing-demo3.zip) <br> [<img width="25" src="colab.svg">](https://colab.research.google.com/github/YData123/sds365-fa24/blob/master/demos/mercer_kernels/mercer-kernel-demo2.ipynb) [Mercer kernels (1/3)](https://github.com/YData123/sds365-fa22/raw/main/demos/mercer_kernels/mercer-kernel-demo2.zip) <br> [<img width="25" src="colab.svg">](https://colab.research.google.com/github/YData123/sds365-fa24/blob/master/demos/mercer_kernels/mercer-kernel-demo.ipynb) [Mercer kernels (2/3)](https://github.com/YData123/sds365-fa24/raw/main/demos/mercer_kernels/mercer-kernel-demo.zip) <br> [<img width="25" src="colab.svg">](https://colab.research.google.com/github/YData123/sds365-fa24/blob/master/demos/mercer_kernels/mercer-kernel-fit-demo.ipynb) [Mercer kernels (3/3)](https://github.com/YData123/sds365-fa24/raw/main/demos/mercer_kernels/mercer-kernel-fit-demo.zip) | Mon: [<span style="color:">Smoothing and density estimation</span>](https://github.com/YData123/sds365-fa24/raw/main/lectures/lecture-sep-9.pdf) <br> Wed: [<span style="color:">Mercer kernels</span>](https://github.com/YData123/sds365-fa24/raw/main/lectures/lecture-sep-11.pdf) |  [Risk bounds for local smoothing](https://github.com/YData123/sds365-fa24/raw/main/notes/kernel-bias-variance.pdf) <br>  [Notes on Mercer kernels](https://github.com/YData123/sds365-fa24/raw/main/notes/mercer-kernels.pdf) |  [<img width="25" src="colab.svg">](https://colab.research.google.com/github/YData123/sds365-fa24/blob/main/assignments/assn1/assn1.ipynb) [<span style="color:">Assn 1 out</span>](https://github.com/YData123/sds365-fa24/raw/main/assignments/assn1/assn1.zip)
 4 | Sep 16, 18 | Neural networks and overparameterized models | [<img width="25" src="colab.svg">](https://colab.research.google.com/github/YData123/sds265-fa21/blob/master/demos/neural-nets/neural-nets-regress.ipynb) [np-complete example (1/2)](https://github.com/YData123/sds265-fa21/raw/main/demos/neural-nets/neural-nets-regress.zip)  <br> [<img width="25" src="colab.svg">](https://colab.research.google.com/github/YData123/sds265-fa21/blob/master/demos/neural-nets/neural-nets.ipynb) [np-complete example (2/2)](https://github.com/YData123/sds265-fa21/raw/main/demos/neural-nets/neural-nets.zip) <br>  [TensorFlow playground](https://playground.tensorflow.org/) | Mon: [<span style="color:">Neural networks</span>](https://github.com/YData123/sds365-fa24/raw/main/lectures/lecture-sep-16.pdf) <br> Wed: [<span style="color:">Double descent</span>](https://github.com/YData123/sds365-fa24/raw/main/lectures/lecture-sep-18.pdf)  | PML Sections 13.1, 13.2 <br> [Notes on backpropagation](https://github.com/YData123/sds265-fa21/raw/main/notes/backprop.pdf) <br> [Notes on double descent](https://github.com/YData123/sds365-fa24/raw/main/notes/double-descent.pdf) | [<span style="color:">Quiz 2</span>](https://yale.instructure.com/courses/98751/quizzes)
 5 | Sep 23, 25 | Convolutional neural networks | [<img width="25" src="colab.svg">](https://colab.research.google.com/github/YData123/sds365-fa24/blob/master/demos/convolution/convolve_demo.ipynb) [Convolution demo](https://github.com/YData123/sds365-fa22/raw/main/demos/convolution/convolve_demo.zip) <br> [<img width="25" src="colab.svg">](https://colab.research.google.com/github/YData123/sds365-fa24/blob/master/demos/convolution/cnn_mnist_demo.ipynb) [CNN demo (1/2)](https://github.com/YData123/sds365-fa22/raw/main/demos/convolution/cnn_mnist_demo.zip) <br> [<img width="25" src="colab.svg">](https://colab.research.google.com/github/YData123/sds365-fa24/blob/master/demos/convolution/brain_food.ipynb) [CNN demo (2/2)](https://github.com/YData123/sds365-fa24/raw/main/demos/convolution/brain_food.zip) |  Mon: [<span style="color:">Convolutional neural networks</span>](https://github.com/YData123/sds365-fa24/raw/main/lectures/lecture-sep-23.pdf) <br> Wed: [<span style="color:">CNNs and Gaussian Processes</span>](https://github.com/YData123/sds365-fa24/raw/main/lectures/lecture-sep-25.pdf)  | PML Section 17.2 <br> [Notes on Bayesian inference](https://github.com/YData123/sds365-fa24/raw/main/notes/bayes-notes.pdf) <br> [Notes on nonparametric Bayes](https://github.com/YData123/sds365-fa24/raw/main/notes/nonparametric-bayes.pdf) |  Assn 1 in <br> [<img width="25" src="colab.svg">](https://colab.research.google.com/github/YData123/sds365-fa24/blob/main/assignments/assn2/assn2.ipynb) [<span style="color:">Assn 2 out</span>](https://github.com/YData123/sds365-fa24/raw/main/assignments/assn2/assn2.zip) 
-6 | Sept 30, Oct 2 | Gaussian processes and approximate inference | [<img width="25" src="colab.svg">](https://colab.research.google.com/github/YData123/sds265-fa21/blob/master/demos/bayes/bayes.ipynb) [Parametric Bayes](https://github.com/YData123/sds265-fa21/raw/main/demos/bayes/bayes.zip) <br>  [<img width="25" src="colab.svg">](https://colab.research.google.com/github/YData123/sds365-fa24/blob/master/demos/gaussian_processes/gp_demo.ipynb) [Gaussian processes](https://github.com/YData123/sds365-fa24/raw/main/demos/gaussian_processes/gp_demo.zip) <br> [<img width="25" src="colab.svg">](https://colab.research.google.com/github/YData123/sds365-fa24/blob/master/demos/gibbs_sampling/gibbs_denoise.ipynb) [Gibbs sampling for image denoising](https://github.com/YData123/sds365-fa22/raw/main/demos/gibbs_sampling/gibbs_denoise.zip)  | Mon: [<span style="color:">Gaussian processes</span>](https://github.com/YData123/sds365-fa24/raw/main/lectures/lecture-sep-30.pdf) <br> Wed: [<span style="color:">Introduction to approximate inference</span>](https://github.com/YData123/sds365-fa24/raw/main/lectures/lecture-oct-2.pdf)  |  [Notes on simulation](https://github.com/YData123/sds365-fa24/raw/main/notes/simulation.pdf) | [<span style="color:gray">Quiz 3</span>](https://yale.instructure.com/courses/98751/quizzes)
+6 | Sept 30, Oct 2 | Gaussian processes and approximate inference | [<img width="25" src="colab.svg">](https://colab.research.google.com/github/YData123/sds265-fa21/blob/master/demos/bayes/bayes.ipynb) [Parametric Bayes](https://github.com/YData123/sds265-fa21/raw/main/demos/bayes/bayes.zip) <br>  [<img width="25" src="colab.svg">](https://colab.research.google.com/github/YData123/sds365-fa24/blob/master/demos/gaussian_processes/gp_demo.ipynb) [Gaussian processes](https://github.com/YData123/sds365-fa24/raw/main/demos/gaussian_processes/gp_demo.zip) <br> [<img width="25" src="colab.svg">](https://colab.research.google.com/github/YData123/sds365-fa24/blob/master/demos/gibbs_sampling/gibbs_denoise.ipynb) [Gibbs sampling for image denoising](https://github.com/YData123/sds365-fa22/raw/main/demos/gibbs_sampling/gibbs_denoise.zip)  | Mon: [<span style="color:">Gaussian processes</span>](https://github.com/YData123/sds365-fa24/raw/main/lectures/lecture-sep-30.pdf) <br> Wed: [<span style="color:">Introduction to approximate inference</span>](https://github.com/YData123/sds365-fa24/raw/main/lectures/lecture-oct-2.pdf)  |  [Notes on simulation](https://github.com/YData123/sds365-fa24/raw/main/notes/simulation.pdf) | [<span style="color:">Quiz 3</span>](https://yale.instructure.com/courses/98751/quizzes)
 7 | Oct 7, 9 | Variational inference | [<img width="25" src="colab.svg">](https://colab.research.google.com/github/YData123/sds365-fa24/blob/master/demos/variational/vae_demo.ipynb) [Variational autoencoders](https://github.com/YData123/sds365-fa22/raw/main/demos/variational/vae_demo.zip) |  Mon: [<span style="color:gray">Variational inference</span>](https://github.com/YData123/sds365-fa24/raw/main/lectures/lecture-oct-9.pdf) <br> Wed: [<span style="color:gray">VAEs</span>](https://github.com/YData123/sds365-fa24/raw/main/lectures/lecture-oct-11.pdf) <br> | PML Section 20.3 <br> [Notes on variational inference](https://github.com/YData123/sds365-fa24/raw/main/notes/variational.pdf)  | Assn 2 in <br>  [<img width="25" src="colab.svg">](https://colab.research.google.com/github/YData123/sds365-fa24/blob/main/assignments/assn3/assn3.ipynb) [<span style="color:gray">Assn 3 out</span>](https://github.com/YData123/sds365-fa24/raw/main/assignments/assn3/assn3.zip)
 8 | Oct 14 | Midterm  | | | [<span style="color:">Practice midterms</span>](https://yale.instructure.com/courses/98751/files/folder/Midterm/practice) | Oct 14: Midterm exam
 9 | Oct 21, 23 | Graphs and structure learning | [<img width="25" src="colab.svg">](https://colab.research.google.com/github/YData123/sds365-fa24/blob/master/demos/graphs/glasso_demo.ipynb) [Graphical lasso demo](https://github.com/YData123/sds365-fa22/raw/main/demos/graphs/glasso_demo.zip) | Mon: [<span style="color:gray">Sparsity and graphs</span>](https://github.com/YData123/sds365-fa24/raw/main/lectures/lecture-oct-23.pdf) <br> Wed: [<span style="color:gray">Discrete data and graph neural nets</span>](https://github.com/YData123/sds365-fa24/raw/main/lectures/lecture-oct-25.pdf) |  [Notes on graphs and structure learning](https://github.com/YData123/sds365-fa24/raw/main/notes/graphs.pdf)  <br> [Graph neural networks](https://distill.pub/2021/understanding-gnns/) <br> PML Section 23.4 |