Joerg Marks
2 years ago
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% Introduction to Data Analysis and Machine Learning in Physics |
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% Martino Borsato, Jörg Marks, Klaus Reygers |
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% 11-14 April 2023 \newline 9:00 - 12:00 and 14:00 - 17:00 |
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## Outline |
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* **Day 1** |
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- Introduction, software and data fitting |
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* **Day 2** |
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- Machine learning - basics |
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* **Day 3** |
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- Machine learning - decision tree |
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* **Day 4** |
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- Machine learning - convolutional networks |
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* **Organization** and **Objective** |
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- \textcolor{red} {2 ETC: Compulsory attendance is required} \newline |
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\textcolor{red} {Active participation in the exercises} |
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- \textcolor{blue}{Course in CIP pool in a tutorial style} |
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- \textcolor{blue}{Obtain basic knowledge for problem-oriented self-studies} |
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## Course Information (1) |
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* Course requirements |
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- Python knowledge needed / good C++ knowledge might work |
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- Userid to use the CIP Pool of the faculty of physics |
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* Course structure |
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- \textcolor{red}{Course in CIP pool} using the \textcolor{red}{jupyter3 hub} |
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- Lectures are interleaved with tutorial/exercise sessions in small groups |
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(up to 5 persons / group) |
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* Course homepage which includes and distributes all material |
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\small |
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[https://www.physi.uni-heidelberg.de/~reygers/lectures/2023/ml/](https://www.physi.uni-heidelberg.de/~reygers/lectures/2023/ml/) \normalsize |
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/transparencies \textcolor{blue}{Transparencies of the lectures} |
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/examples \textcolor{blue}{iPython files shown in the lectures} |
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/exercises \textcolor{blue}{Exercises to be solved during the course} |
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/solutions \textcolor{blue}{Solutions of the exercises} |
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## Course Information (2) |
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`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: |
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\vspace{3ex} |
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[https://colab.research.google.com/](https://colab.research.google.com/) |
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\vfill |
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Missing python libraries can be included by adding the following to a cell (here for the pypng library): |
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``` |
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!pip install pypng |
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``` |
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## Course Information (3) |
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* Your installation at home: |
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* \textcolor{blue}{Web Browser to access jupyter3} |
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* \textcolor{blue}{Access to the CIP pool via an ssh client on your home PC} |
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* No requirements for a special operating system |
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* Software: |
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* firefox or similar |
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* Cisco AnyConnect |
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* ssh client (MobaXterm on Windows, integrated in Linux/Mac) |
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* Local execution of python / iPython |
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* Install ``anaconda3`` and download / run the iPython notebooks (also python scripts are available) |
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* \textcolor{red}{Hints for software installations and CIP pool access} |
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\small |
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[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 |
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## Course Information (4) |
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Alternatively, you can install the libraries needed on your local computer. |
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\vfill |
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Here are the relevant instruction for macOS using `pip`: |
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\vfill |
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Assumptions: `homebrew` is installed. |
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\vfill |
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Install python3 (see https://docs.python-guide.org/starting/install3/osx/) |
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\footnotesize |
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``` |
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$ brew install python |
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$ python --version |
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Python 3.8.5 |
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``` |
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\normalsize |
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Make sure pip3 is up-to-date (alternative: conda $\rightarrow$ don't mix conda and pip installations) |
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\footnotesize |
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``` |
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$ pip3 install --upgrade pip |
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``` |
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\normalsize |
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Install modules needed: |
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\footnotesize |
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``` |
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$ pip3 install --upgrade jupyter matplotlib numpy pandas |
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scipy scikit-learn xgboost iminuit tensorflow tensorflow_datasets Keras |
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``` |
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\normalsize |
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## Topcics and file name conventions |
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0. Introduction (this file) \hspace{0.1cm} \footnotesize (\textcolor{gray}{introduction.pdf}) \normalsize |
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1. Introduction to python \hspace{0.1cm} \footnotesize (\textcolor{gray}{01\_intro\_python\_*}) \normalsize |
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2. Data modeling and fitting \hspace{0.1cm} \footnotesize (\textcolor{gray}{02\_fit\_intro\_*}) \normalsize |
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3. Machine learning basics \hspace{0.1cm} \footnotesize (\textcolor{gray}{03\_ml\_basics\_*}) \normalsize |
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4. Decisions trees \hspace{0.1cm} \footnotesize (\textcolor{gray}{04\_decision\_trees\_*}) \normalsize |
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5. Neural networks \hspace{0.1cm} \footnotesize (\textcolor{gray}{05\_neural\_networks\_*}) \normalsize |
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\vspace{3.5cm} |
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## Programm Day 1 |
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* Technicalities |
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* Summary of NumPy |
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* Plotting with matplotlib |
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* Input / output of data |
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* Summary of pandas |
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* Fitting with iminuit and PyROOT |
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* Transparencies with activated links, examples and exercises |
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* Software: [\textcolor{violet}{01\_intro\_python.pdf}](https://www.physi.uni-heidelberg.de/~reygers/lectures/2023/ml/transparencies/01_intro_python.pdf) |
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* Fitting: |
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[\textcolor{violet}{02\_fit\_intro.pdf}](https://www.physi.uni-heidelberg.de/~reygers/lectures/2023/ml/transparencies/02_fit_intro.pdf) |
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\vspace{2cm} |
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## Programm Day 2 |
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* Supervised learning |
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* Classification and regression |
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* Linear regression |
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* Logistic regression |
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* Softmax regression (multi-class classification) |
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\vspace{4cm} |
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## Programm Day 3 |
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* Decision trees |
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* Bagging and boosting |
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* Random forest |
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* XGBoost |
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\vspace{5cm} |
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## Programm Day 4 |
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* Neural networks |
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* Convolutional neural networks |
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* TensorFlow and Keras |
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* Hand-written digit recognition with Keras |
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\vspace{5cm} |
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