% Introduction to Data Analysis and Machine Learning in Physics
% Jörg Marks, Klaus Reygers
% 11-14 April 2023 \newline 9:00 - 12:00 and 14:00 - 17:00


## Outline

 * **Day 1**
   - Introduction, software and data fitting

 * **Day 2**
   - Machine learning - basics
   
 * **Day 3**
   - Machine learning - decision trees

 * **Day 4**
   - Machine learning - convolutional networks and graph neural networks

 * **Organization** and **Objective**
   -  \textcolor{red} {2 ETC: Compulsory attendance is required} \newline
      \textcolor{red} {Active participation in the exercises} 
   -  \textcolor{blue}{Course in CIP pool in a tutorial style}
   -  \textcolor{blue}{Obtain basic knowledge for problem-oriented self-studies}
   
## Course Information (1)

 * Course requirements

   - Python knowledge needed / good C++ knowledge might work
   - Userid to use the CIP Pool of the faculty of physics 

 * Course structure
   - \textcolor{red}{Course in CIP pool} using the \textcolor{red}{jupyter3 hub}
   - Lectures are interleaved with tutorial/exercise sessions in small groups
     (up to 5 persons / group)
 
 * Course homepage which includes and distributes all material
 \small
   [https://www.physi.uni-heidelberg.de/~reygers/lectures/2023/ml/](https://www.physi.uni-heidelberg.de/~reygers/lectures/2023/ml/) \normalsize

   /transparencies        \textcolor{blue}{Transparencies of the lectures}
   
   /examples             \textcolor{blue}{iPython files shown in the lectures}
   
   /exercises              \textcolor{blue}{Exercises to be solved during the course}
   
   /solutions             \textcolor{blue}{Solutions of the exercises}


## Course Information (2)

`TensorFlow` and `Keras` are now also installed in the CIP jupyter hub. In addition, with a google account you can run jupyter notebooks on Google Colab:

\vspace{3ex}
[https://colab.research.google.com/](https://colab.research.google.com/)

\vfill

Missing python libraries can be included by adding the following to a cell (here for the pypng library):

```
!pip install pypng
```


## Course Information (3) 

 * Your installation at home:
   * \textcolor{blue}{Web Browser to access jupyter3}
   * \textcolor{blue}{Access to the CIP pool via an ssh client on your home PC}
   
 * No requirements for a special operating system

 * Software:
   * firefox or similar
   * Cisco AnyConnect
   * ssh client (MobaXterm on Windows, integrated in Linux/Mac)

 * Local execution of python / iPython
   * Install ``anaconda3`` and download / run the iPython notebooks (also python  scripts are available)

 *  \textcolor{red}{Hints for software installations and CIP pool access}
    \small
    
     [https://www.physi.uni-heidelberg.de/~marks/root_einfuehrung/Folien/CIPpoolAccess.PDF](https://www.physi.uni-heidelberg.de/~marks/root_einfuehrung/Folien/CIPpoolAccess.PDF) \normalsize
 
## Course Information (4)
Alternatively, you can install the libraries needed on your local computer.

\vfill

Here are the relevant instruction for macOS using `pip`:

\vfill

Assumptions: `homebrew` is installed.

\vfill

Install python3 (see https://docs.python-guide.org/starting/install3/osx/)
\footnotesize
```
$ brew install python
$ python --version
Python 3.8.5
```
\normalsize


Make sure pip3 is up-to-date (alternative: conda  $\rightarrow$ don't mix conda and pip installations)
\footnotesize
```
$ pip3 install --upgrade pip
```
\normalsize


Install modules needed:
\footnotesize
```
$ pip3 install --upgrade jupyter matplotlib numpy pandas 
scipy scikit-learn xgboost iminuit tensorflow tensorflow_datasets Keras
```
\normalsize




## Topcics and file name conventions

0. Introduction (this file) \hspace{0.1cm} \footnotesize (\textcolor{gray}{introduction.pdf}) \normalsize
1. Introduction to python \hspace{0.1cm} \footnotesize (\textcolor{gray}{01\_intro\_python\_*}) \normalsize
2. Data modeling and fitting \hspace{0.1cm} \footnotesize (\textcolor{gray}{02\_fit\_intro\_*}) \normalsize
3. Machine learning basics \hspace{0.1cm} \footnotesize (\textcolor{gray}{03\_ml\_basics\_*}) \normalsize
4. Decisions trees \hspace{0.1cm} \footnotesize (\textcolor{gray}{04\_decision\_trees\_*}) \normalsize
5. Neural networks \hspace{0.1cm} \footnotesize (\textcolor{gray}{05\_neural\_networks\_*}) \normalsize
\vspace{3.5cm}

## Programm Day 1



* Technicalities

* Summary of NumPy

* Plotting with matplotlib

* Input / output of data

* Summary of pandas

* Fitting with iminuit and PyROOT

* Transparencies with activated links, examples and exercises

  * Software:  [\textcolor{violet}{01\_intro\_python.pdf}](https://www.physi.uni-heidelberg.de/~reygers/lectures/2023/ml/transparencies/01_intro_python.pdf)

  * Fitting:
    [\textcolor{violet}{02\_fit\_intro.pdf}](https://www.physi.uni-heidelberg.de/~reygers/lectures/2023/ml/transparencies/02_fit_intro.pdf)
    
 \vspace{2cm}


## Programm Day 2

* Introduction to machine learning  [\textcolor{violet}{03\_ml\_intro.pdf}](https://www.physi.uni-heidelberg.de/~reygers/lectures/2023/ml/transparencies/03_ml_intro.pdf)

  * Tensorflow / Keras, datasets  
  * Supervised learning
  * Classification

 \vspace{0.5cm}

* Multivariate analysis [\textcolor{violet}{03\_ml\_intro\_mva.pdf}](https://www.physi.uni-heidelberg.de/~reygers/lectures/2023/ml/transparencies/03_ml_intro_mva.pdf)

  * Regression
  * Linear regression
  * Logistic regression
  * Softmax regression (multi-class classification)

\vspace{4cm}

## Programm Day 3

* Decision trees

* Bagging and boosting

* Random forest

* XGBoost

  \vspace{0.5cm}
 
  [\textcolor{violet}{04\_decision\_trees.pdf}](https://www.physi.uni-heidelberg.de/~reygers/lectures/2023/ml/transparencies/04_decision_trees.pdf)

\vspace{5cm}

## Programm Day 4

* Neural networks

* Convolutional neural networks

* Hand-written digit recognition with Keras


  \vspace{0.5cm}
 
  [\textcolor{violet}{05\_neural\_networks.pdf}](https://www.physi.uni-heidelberg.de/~reygers/lectures/2023/ml/transparencies/05_neural_networks.pdf)


\vspace{5cm}