updated README

README.md

@@ -1,35 +1,33 @@
-## Adaptive Information Processing
+## Bayesian Machine Learning and Information Processing
 
-**Bert de Vries** and **Tjalling Tjalkens**.    
-Eindhoven University of Technology, Dept. of Electrical Engineering .    
+[Bert de Vries](http://bertdv.nl) and [Wouter Kouw](https://biaslab.github.io/member/wouter/)                     
+Eindhoven University of Technology, Dept. of Electrical Engineering        
 Corr. to <bert.de.vries@tue.nl>
 
-This site contains materials for course [5SSB0 (Adaptive Information Processing)](http://5SSB0.nl) at [TU/e](http://tue.nl).
+This site contains materials for course [5SSD0 (Bayesian Machine Learning and Information Processing)](https://github.com/bertdv/BMLIP) at [TU/e](http://tue.nl).
 
+### Teaching assistants
+[Ismail Senoz](https://biaslab.github.io/member/ismail/) and [Magnus Koudahl](https://biaslab.github.io/member/magnus/)
 
 ### 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)
-
-<!---
-- [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!)
---->
+- [0 - Course Outline and Administrative Issues](http://nbviewer.ipython.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/Course-Outline.ipynb)
+- [1 - Machine Learning Overview](http://nbviewer.ipython.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/Machine-Learning-Overview.ipynb)
+- [2 - Probability Theory Review](http://nbviewer.ipython.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/Probability-Review.ipynb)
+- [3 - Bayesian Machine Learning](http://nbviewer.ipython.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/Bayesian-Machine-Learning.ipynb)
+- [4 - Working with Gaussians and Multinomials](http://nbviewer.ipython.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/Working-with-Gaussians.ipynb)
+- [5 - Simple Density Estimation](http://nbviewer.ipython.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/Density-Estimation.ipynb)
+- [6 - Regression](http://nbviewer.ipython.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/Regression.ipynb)
+- [7 - Classification](http://nbviewer.ipython.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/Classification.ipynb)
+- [9 - Latent Variable Models and Variational Bayes](http://nbviewer.ipython.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/Latent-Variable-Models-and-VB.ipynb)
+- [10- Probabilistic Programming](http://nbviewer.ipython.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/Probabilistic-Programming.ipynb)
+- [11- Dynamic Models](http://nbviewer.ipython.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/Dynamic-Models.ipynb)
+- [12- Intelligent Agents and Active Inference](http://nbviewer.ipython.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/Intelligent-Agents-and-Active-Inference.ipynb)
+- [13- Applications](http://nbviewer.ipython.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/Applications.ipynb)
+- [14- What is Life?](http://nbviewer.ipython.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/What-if-Life.ipynb)
 
 ### Opening the lecture notes locally
 
@@ -47,65 +45,22 @@ To run the interactive code examples in the lecture nodes, the following Julia p
 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 build -t BMLIP-bundler .
 $ docker run --rm \
-             --volume ${PWD}/lessons:/aip-5ssb0-bundler/lessons \
-             --volume ${PWD}/output:/aip-5ssb0-bundler/output \
-             aip-5ssb0-bundler
+             --volume ${PWD}/lessons:/BMLIP-bundler/lessons \
+             --volume ${PWD}/output:/BMLIP-bundler/output \
+             BMLIP-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
 
-<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" property="dct:title">Adaptive Information Processing (5SSB0)</span> by <span xmlns:cc="http://creativecommons.org/ns#" property="cc:attributionName">Bert de Vries, Tjalling Tjalkens and Marco Cox</span> is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/">Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License</a>
+<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" property="dct:title">Bayesian Machine Learning and Information Processing</span> by <span xmlns:cc="http://creativecommons.org/ns#" property="cc:attributionName">Bert de Vries</span> is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/">Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License</a>