diff --git a/fa24/interml/calendar.md b/fa24/interml/calendar.md index 4e2402f7..d6e08f44 100644 --- a/fa24/interml/calendar.md +++ b/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 | [](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)
[](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)
[](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)
[](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: [Smoothing and density estimation](https://github.com/YData123/sds365-fa24/raw/main/lectures/lecture-sep-9.pdf)
Wed: [Mercer kernels](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)
[Notes on Mercer kernels](https://github.com/YData123/sds365-fa24/raw/main/notes/mercer-kernels.pdf) | [](https://colab.research.google.com/github/YData123/sds365-fa24/blob/main/assignments/assn1/assn1.ipynb) [Assn 1 out](https://github.com/YData123/sds365-fa24/raw/main/assignments/assn1/assn1.zip) 4 | Sep 16, 18 | Neural networks and overparameterized models | [](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)
[](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)
[TensorFlow playground](https://playground.tensorflow.org/) | Mon: [Neural networks](https://github.com/YData123/sds365-fa24/raw/main/lectures/lecture-sep-16.pdf)
Wed: [Double descent](https://github.com/YData123/sds365-fa24/raw/main/lectures/lecture-sep-18.pdf) | PML Sections 13.1, 13.2
[Notes on backpropagation](https://github.com/YData123/sds265-fa21/raw/main/notes/backprop.pdf)
[Notes on double descent](https://github.com/YData123/sds365-fa24/raw/main/notes/double-descent.pdf) | [Quiz 2](https://yale.instructure.com/courses/98751/quizzes) 5 | Sep 23, 25 | Convolutional neural networks | [](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)
[](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)
[](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: [Convolutional neural networks](https://github.com/YData123/sds365-fa24/raw/main/lectures/lecture-sep-23.pdf)
Wed: [CNNs and Gaussian Processes](https://github.com/YData123/sds365-fa24/raw/main/lectures/lecture-sep-25.pdf) | PML Section 17.2
[Notes on Bayesian inference](https://github.com/YData123/sds365-fa24/raw/main/notes/bayes-notes.pdf)
[Notes on nonparametric Bayes](https://github.com/YData123/sds365-fa24/raw/main/notes/nonparametric-bayes.pdf) | Assn 1 in
[](https://colab.research.google.com/github/YData123/sds365-fa24/blob/main/assignments/assn2/assn2.ipynb) [Assn 2 out](https://github.com/YData123/sds365-fa24/raw/main/assignments/assn2/assn2.zip) -6 | Sept 30, Oct 2 | Gaussian processes and approximate inference | [](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)
[](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)
[](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: [Gaussian processes](https://github.com/YData123/sds365-fa24/raw/main/lectures/lecture-sep-30.pdf)
Wed: [Introduction to approximate inference](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) | [Quiz 3](https://yale.instructure.com/courses/98751/quizzes) +6 | Sept 30, Oct 2 | Gaussian processes and approximate inference | [](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)
[](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)
[](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: [Gaussian processes](https://github.com/YData123/sds365-fa24/raw/main/lectures/lecture-sep-30.pdf)
Wed: [Introduction to approximate inference](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) | [Quiz 3](https://yale.instructure.com/courses/98751/quizzes) 7 | Oct 7, 9 | Variational inference | [](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: [Variational inference](https://github.com/YData123/sds365-fa24/raw/main/lectures/lecture-oct-9.pdf)
Wed: [VAEs](https://github.com/YData123/sds365-fa24/raw/main/lectures/lecture-oct-11.pdf)
| PML Section 20.3
[Notes on variational inference](https://github.com/YData123/sds365-fa24/raw/main/notes/variational.pdf) | Assn 2 in
[](https://colab.research.google.com/github/YData123/sds365-fa24/blob/main/assignments/assn3/assn3.ipynb) [Assn 3 out](https://github.com/YData123/sds365-fa24/raw/main/assignments/assn3/assn3.zip) 8 | Oct 14 | Midterm | | | [Practice midterms](https://yale.instructure.com/courses/98751/files/folder/Midterm/practice) | Oct 14: Midterm exam 9 | Oct 21, 23 | Graphs and structure learning | [](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: [Sparsity and graphs](https://github.com/YData123/sds365-fa24/raw/main/lectures/lecture-oct-23.pdf)
Wed: [Discrete data and graph neural nets](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)
[Graph neural networks](https://distill.pub/2021/understanding-gnns/)
PML Section 23.4 |