{"id":23617348,"url":"https://github.com/bertdv/aip-5ssb0","last_synced_at":"2025-05-13T00:24:32.072Z","repository":{"id":18498898,"uuid":"21694975","full_name":"bertdv/AIP-5SSB0","owner":"bertdv","description":"5SSB0 -- Adaptive Information Processing course notes","archived":false,"fork":false,"pushed_at":"2020-08-22T09:00:09.000Z","size":157592,"stargazers_count":11,"open_issues_count":2,"forks_count":5,"subscribers_count":5,"default_branch":"master","last_synced_at":"2024-12-22T22:53:22.918Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/bertdv.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2014-07-10T13:22:34.000Z","updated_at":"2023-11-22T01:52:46.000Z","dependencies_parsed_at":"2022-07-30T13:49:24.695Z","dependency_job_id":null,"html_url":"https://github.com/bertdv/AIP-5SSB0","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bertdv%2FAIP-5SSB0","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bertdv%2FAIP-5SSB0/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bertdv%2FAIP-5SSB0/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bertdv%2FAIP-5SSB0/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/bertdv","download_url":"https://codeload.github.com/bertdv/AIP-5SSB0/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":231522582,"owners_count":18389563,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-12-27T18:20:31.658Z","updated_at":"2024-12-27T18:20:32.158Z","avatar_url":"https://github.com/bertdv.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Adaptive Information Processing\n\n**Bert de Vries** and **Tjalling Tjalkens**.    \nEindhoven University of Technology, Dept. of Electrical Engineering .    \nCorr. to \u003cbert.de.vries@tue.nl\u003e\n\nThis site contains materials for course [5SSB0 (Adaptive Information Processing)](http://5SSB0.nl) at [TU/e](http://tue.nl).\n\n\n### Read-only versions\n\nYou can view the lecture notes through the links below:\n\n\n- [ 0 - Introduction](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/00_Introduction.ipynb)\n- [1 - Machine Learning Overview](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/01_Machine-Learning-Overview.ipynb)\n- [2 - Probability Theory Review](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/02_Probability-Review.ipynb)\n- [3 - Bayesian Machine Learning](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/03_Bayesian-Machine-Learning.ipynb)\n- [4 - Working with Gaussians](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/04_Working-with-Gaussians.ipynb)\n- [5 - Density Estimation](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/05_Density-Estimation.ipynb)\n- [6 - Linear Regression](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/06_Linear-Regression.ipynb)\n- [7 - Generative Classification](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/07_Generative-Classification.ipynb)\n- [8 - Discriminative Classification](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/08_Discriminative-Classification.ipynb)\n- [9 - Clustering with Gaussian Mixture Models](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/09_Clustering-with-Gaussian-Mixture-Models.ipynb)\n- [10- The EM Algorithm](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/10_The-General-EM-Algorithm.ipynb)\n- [11- Continuous Latent Variable Models - PCA and FA](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/11_Continuous-Latent-Variable-Models-PCA-and-FA.ipynb)\n- [12- Dynamic Latent Variable Models](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/12_Dynamic-Latent-Variable-Models.ipynb)\n- [13- Factor Graphs and Message Passing Algorithms](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/13_Factor-Graphs-and-Message-Passing-Algorithms.ipynb)\n\n\u003c!---\n- [14- EM as a Message Passing Algorithm](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/14_EM-as-Message-Passing.ipynb) (this lesson not at exam!)\n---\u003e\n\n### Opening the lecture notes locally\n\nTo open the lecture notes in [IJulia](https://github.com/JuliaLang/IJulia.jl), download the .ipynb files to your computer and start a [Jupyter](https://jupyter.org/) notebook by\n\n```\n$ jupyter notebook\n```\n\nYou will now get a new page in your browser with a list of available notebooks. Alternatively, if you don't have Julia/Jupyter installed on your system, you can use [JuliaBox](https://www.juliabox.com/) to run the notebooks (see exact instructions below).\n\nTo run the interactive code examples in the lecture nodes, the following Julia packages are required: `Cubature`, `DataFrames`, `Distributions`, `Interact`, `PyPlot`, `Optim`. To install the required packages, execute:\n\n```jl\nmap(Pkg.add, [\"Cubature\", \"DataFrames\", \"CSV\", \"Distributions\", \"Interact\", \"PyPlot\", \"Optim\", \"SpecialFunctions\"])\n```\n\n### Running the lecture notes on JuliaBox.com\n\nFollow these instructions to run the code examples from the lecture notes online through [JuliaBox](https://www.juliabox.com/).\n\n1. **(Create account)**\n   Go to https://www.juliabox.com/, create an account and log in.\n\n2. **(Install required packages)**\n   Go to the `Console` tab, and then start a Julia shell by typing `julia`.\n\n   In the Julia shell, execute the following command to install all required packages:\n\n   ```jl\n   map(Pkg.add, [\"Cubature\", \"DataFrames\", \"CSV\", \"Distributions\", \"Interact\", \"PyPlot\", \"Optim\", \"SpecialFunctions\"])\n   ```\n\n   Afterwards, type `exit()` to quit Julia.\n\n3. **(Import lecture notes into JuliaBox)**\n   Go to the `Sync` tab, and add the lecture notes git repository through the following actions:\n 1. Paste `https://github.com/bertdv/AIP-5SSB0.git` in the `Git Clone URL` field\n 2. Click with the mouse in the `branch` field. You should get `master` in the `branch` field and `AIP-5SSB0` in the `juliabox` field.\n 3. Press the \"+\" button.\n\nYou can now open the lecture notes by going to the `Jupyter` tab (press the refresh button if the folder `AIP-5SSB0` does not show up). Navigate to a specific lesson and click the `.ipynb` file to open the notebook.\n\n### Creating a PDF bundle of all lessons\n\nInstall Docker from https://www.docker.com.\n\nFinally from the root directory of the project issue\n\n```sh\n$ docker build -t aip-5ssb0-bundler .\n$ docker run --rm \\\n             --volume ${PWD}/lessons:/aip-5ssb0-bundler/lessons \\\n             --volume ${PWD}/output:/aip-5ssb0-bundler/output \\\n             aip-5ssb0-bundler\n```\n\nto obtain a `bundle.pdf` file containing all lessons in the `output` directory.\n\n#### Running Jupyter using the Docker image\n\nSometimes it may be convenient or necessary to get access to\nJupyter while it's running inside the Docker image. The\nfollowing procedure can be used to achieve this:\n\n```sh\n$ docker run --rm -it \\\n             --volume ${PWD}/lessons:/aip-5ssb0-bundler/lessons \\\n             --volume ${PWD}/output:/aip-5ssb0-bundler/output \\\n             --publish 8888:8888 \\\n             aip-5ssb0-bundler jupyter notebook --ip 0.0.0.0\n```\n\nThen open the URL Jupyter reports in a browser, substituting\n`0.0.0.0` with `localhost`.\n\n#### License\n\n\u003ca rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-nd/4.0/\"\u003e\u003cimg alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png\" /\u003e\u003c/a\u003e\u003cbr /\u003e\u003cspan xmlns:dct=\"http://purl.org/dc/terms/\" property=\"dct:title\"\u003eAdaptive Information Processing (5SSB0)\u003c/span\u003e by \u003cspan xmlns:cc=\"http://creativecommons.org/ns#\" property=\"cc:attributionName\"\u003eBert de Vries, Tjalling Tjalkens and Marco Cox\u003c/span\u003e is licensed under a \u003ca rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-nd/4.0/\"\u003eCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License\u003c/a\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbertdv%2Faip-5ssb0","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbertdv%2Faip-5ssb0","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbertdv%2Faip-5ssb0/lists"}