https://github.com/bertdv/aip-5ssb0
5SSB0 -- Adaptive Information Processing course notes
https://github.com/bertdv/aip-5ssb0
Last synced: about 1 year ago
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5SSB0 -- Adaptive Information Processing course notes
- Host: GitHub
- URL: https://github.com/bertdv/aip-5ssb0
- Owner: bertdv
- License: other
- Created: 2014-07-10T13:22:34.000Z (almost 12 years ago)
- Default Branch: master
- Last Pushed: 2020-08-22T09:00:09.000Z (almost 6 years ago)
- Last Synced: 2024-12-22T22:53:22.918Z (over 1 year ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 150 MB
- Stars: 11
- Watchers: 5
- Forks: 5
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Adaptive Information Processing
**Bert de Vries** and **Tjalling Tjalkens**.
Eindhoven University of Technology, Dept. of Electrical Engineering .
Corr. to
This site contains materials for course [5SSB0 (Adaptive Information Processing)](http://5SSB0.nl) at [TU/e](http://tue.nl).
### Read-only versions
You can view the lecture notes through the links below:
- [ 0 - Introduction](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/00_Introduction.ipynb)
- [1 - Machine Learning Overview](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/01_Machine-Learning-Overview.ipynb)
- [2 - Probability Theory Review](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/02_Probability-Review.ipynb)
- [3 - Bayesian Machine Learning](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/03_Bayesian-Machine-Learning.ipynb)
- [4 - Working with Gaussians](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/04_Working-with-Gaussians.ipynb)
- [5 - Density Estimation](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/05_Density-Estimation.ipynb)
- [6 - Linear Regression](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/06_Linear-Regression.ipynb)
- [7 - Generative Classification](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/07_Generative-Classification.ipynb)
- [8 - Discriminative Classification](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/08_Discriminative-Classification.ipynb)
- [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)
- [10- The EM Algorithm](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/10_The-General-EM-Algorithm.ipynb)
- [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)
- [12- Dynamic Latent Variable Models](http://nbviewer.ipython.org/github/bertdv/AIP-5SSB0/blob/master/lessons/notebooks/12_Dynamic-Latent-Variable-Models.ipynb)
- [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)
### Opening the lecture notes locally
To 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
```
$ jupyter notebook
```
You 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).
To 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:
```jl
map(Pkg.add, ["Cubature", "DataFrames", "CSV", "Distributions", "Interact", "PyPlot", "Optim", "SpecialFunctions"])
```
### Running the lecture notes on JuliaBox.com
Follow these instructions to run the code examples from the lecture notes online through [JuliaBox](https://www.juliabox.com/).
1. **(Create account)**
Go to https://www.juliabox.com/, create an account and log in.
2. **(Install required packages)**
Go to the `Console` tab, and then start a Julia shell by typing `julia`.
In the Julia shell, execute the following command to install all required packages:
```jl
map(Pkg.add, ["Cubature", "DataFrames", "CSV", "Distributions", "Interact", "PyPlot", "Optim", "SpecialFunctions"])
```
Afterwards, type `exit()` to quit Julia.
3. **(Import lecture notes into JuliaBox)**
Go to the `Sync` tab, and add the lecture notes git repository through the following actions:
1. Paste `https://github.com/bertdv/AIP-5SSB0.git` in the `Git Clone URL` field
2. Click with the mouse in the `branch` field. You should get `master` in the `branch` field and `AIP-5SSB0` in the `juliabox` field.
3. Press the "+" button.
You 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.
### Creating a PDF bundle of all lessons
Install Docker from https://www.docker.com.
Finally from the root directory of the project issue
```sh
$ docker build -t aip-5ssb0-bundler .
$ docker run --rm \
--volume ${PWD}/lessons:/aip-5ssb0-bundler/lessons \
--volume ${PWD}/output:/aip-5ssb0-bundler/output \
aip-5ssb0-bundler
```
to obtain a `bundle.pdf` file containing all lessons in the `output` directory.
#### Running Jupyter using the Docker image
Sometimes it may be convenient or necessary to get access to
Jupyter while it's running inside the Docker image. The
following procedure can be used to achieve this:
```sh
$ docker run --rm -it \
--volume ${PWD}/lessons:/aip-5ssb0-bundler/lessons \
--volume ${PWD}/output:/aip-5ssb0-bundler/output \
--publish 8888:8888 \
aip-5ssb0-bundler jupyter notebook --ip 0.0.0.0
```
Then open the URL Jupyter reports in a browser, substituting
`0.0.0.0` with `localhost`.
#### License
Adaptive Information Processing (5SSB0) by Bert de Vries, Tjalling Tjalkens and Marco Cox is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License