ML-Kurs-SS2023/slides/introduction.md

% 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/ \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/

\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 \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}

  • Fitting: \textcolor{violet}{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}