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+% Introduction to Data Analysis and Machine Learning in Physics
+% Martino Borsato, 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 tree
+
+ * **Day 4**
+   - Machine learning - convolutional 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
+
+* Supervised learning
+
+* Classification and regression
+
+* Linear regression
+
+* Logistic regression
+
+* Softmax regression (multi-class classification)
+
+\vspace{4cm}
+
+## Programm Day 3
+
+* Decision trees
+
+* Bagging and boosting
+
+* Random forest
+
+* XGBoost
+
+\vspace{5cm}
+
+## Programm Day 4
+
+* Neural networks
+
+* Convolutional neural networks
+
+* TensorFlow and Keras
+
+* Hand-written digit recognition with Keras
+
+\vspace{5cm}