reformatted proposal

.gitignore

+.ipynb_checkpoints/
+venv*

content.md

-# Targeted audience
-
-- Researchers having no machine learning experience yet.
-- Basic Python knowledge.
-- Almost no math knowledge required.
-
-# Course structure
-
-- Two days workshop, 1.5 days workshop + .5 day working on own data / prepared data.
-- Every part below includes a coding session using Jupyter notebooks.
-- Coding sessions provide code frames which should be completed.
-- We provide solutions.
-
-
-# Day 1
-
-## Part 0: Preparation
-
-- Quick basics matplotlib, numpy, pandas?
-
-
-### Coding session
-
-- read dataframe from csv or excel sheet with beer features
-- do some features vs features scatter plots
-
-
-## Part 1: Introduction
-
-- What is machine learning ?
-- What are features / samples / feature matrix ?
-- Learning problems: supervised / unsupervised
-
-
-### Code walkthrough:
-
-  - Classification: linear SVM classifier or logistic regression example
-  - Clustering: scikit-learn example to find clusters.
-
-
-## Part 2: classification
-
-  Intention: demonstrate one / two simple examples of classifiers, also
-             introduce the concept of decision boundary
-
-  - Introduction: some simple two dimensional examples incl. decision function.
-
-  - Idea of linear classifier:
-    - simple linear classifier (linear SVM e.g.)
-    - beer example with some weights
-
-  - Discuss code example with logistic regression for beer data, show weights
-
-### Coding session:
-
-  - Change given code to use a linear SVM classifier
-  - Use different data set which can not be classified well with a linear classifier
-
-
-## Part 3: accuracy, F1, ROC, ...
-
-Intention: accuracy is useful but has pitfalls
-
-- how to measure accuracy ?
-
-    - confusion matrix
-    - accurarcy
-    - pitfalls for unbalanced data sets
-        e.g. diagnose HIV
-    - precision / recall
-
-### Coding session
-
-- Evaluate accuracy of linear beer classifier from latest section
-- Determine precision / recall
-
-
-## Part 4: underfitting/overfitting
-
-classifiers / regressors have parameters / degrees of freedom.
-
-- underfitting: linear classifier on nonlinear problem
-
-- overfitting:
-
-  - features have actual noise, or not enough information: orchid example in 2d. elevate to 3d using another feature.
-  - polynome of degree 5 to fit points on a line + noise
-  - points in a circle: draw very exact boundary line
-
-- how to check underfitting / overfitting ?
-
-  - measure accuracy or other metric on test dataset
-  - cross validation
-
-
-### Coding session:
-
-- How to do cross validation with scikit-learn
-- run cross validation on classifier for beer data
-
-
-## Part 5: pipelines / parameter tuning with scikit-learn
-
-- Scikit learn API incl. summary of what we have seen up to now.
-- pipelines, preprocessing (scaler, PCA)
-- cross validation
-- Hyper parameter tuning: grid search / random search.
-
-### Coding session
-
-- examples
-
-
-# DAY 2
-
-## Part 6: Overview classifiers
-
-- Nearest neighbours
-- SVMs
-  - demo for RBF: different parameters influence on decision line
-- Random forests
-- Gradient Tree Boosting
-
-
-### Coding session
-
-- Prepare examples for 2d classification problems incl. visualization of different
-  decision surfaces.
-
-- Play with different classifiers on beer data
-
-## Part 7: Regression
-
-- What are differences compared to classification: output, how to measure accuracy, ...
-
-- Example: fit polynomial, examples for underfitting and overfitting
-
-
-### Coding session
-
-Introduce movie data set, learn SVR or other regressor on this data set.
-
-
-## Part 8: Introduction neural networks
-
-
-- Overview of the field
-- Introduction to feed forward neural networks
-- Demo Keras
-
-### Coding Session
-
-- keras reuse network and play with it.
-
-
-## Workshop
-
-- assist to setup the workshop material on own computer.
-- provide example problems if attendees don't bring own data.
-
-

machine_learning_workshop_proposal.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction to machine-learning with Python\n",
+    "\n",
+    "\n",
+    "\n",
+    "### Targeted audience\n",
+    "\n",
+    "- Researchers having no machine learning experience yet.\n",
+    "- Basic Python knowledge.\n",
+    "- Almost no math knowledge required.\n",
+    "\n",
+    "### Course structure\n",
+    "\n",
+    "- Two days workshop, 1.5 days workshop + .5 day working on own data / prepared data.\n",
+    "- Every part below includes a coding session using Jupyter notebooks.\n",
+    "- Coding sessions provide code frames which should be completed.\n",
+    "- We provide solutions.\n",
+    "\n",
+    "\n",
+    "## Day 1\n",
+    "\n",
+    "### Part 0: Preparation\n",
+    "\n",
+    "- Quick basics matplotlib, numpy, pandas?\n",
+    "\n",
+    "\n",
+    "#### Coding session\n",
+    "\n",
+    "- read dataframe from csv or excel sheet with beer features\n",
+    "- do some features vs features scatter plots\n",
+    "\n",
+    "\n",
+    "### Part 1: Introduction\n",
+    "\n",
+    "- What is machine learning ?\n",
+    "- What are features / samples / feature matrix ?\n",
+    "- Learning problems: supervised / unsupervised\n",
+    "\n",
+    "\n",
+    "#### Code walkthrough:\n",
+    "\n",
+    "  - Classification: linear SVM classifier or logistic regression example\n",
+    "  - Clustering: scikit-learn example to find clusters.\n",
+    "\n",
+    "\n",
+    "### Part 2: classification\n",
+    "\n",
+    "  Intention: demonstrate one / two simple examples of classifiers, also\n",
+    "             introduce the concept of decision boundary\n",
+    "\n",
+    "  - Introduction: some simple two dimensional examples incl. decision function.\n",
+    "\n",
+    "  - Idea of linear classifier:\n",
+    "    - simple linear classifier (linear SVM e.g.)\n",
+    "    - beer example with some weights\n",
+    "\n",
+    "  - Discuss code example with logistic regression for beer data, show weights\n",
+    "\n",
+    "#### Coding session:\n",
+    "\n",
+    "  - Change given code to use a linear SVM classifier\n",
+    "  - Use different data set which can not be classified well with a linear classifier\n",
+    "\n",
+    "\n",
+    "### Part 3: accuracy, F1, ROC, ...\n",
+    "\n",
+    "Intention: accuracy is useful but has pitfalls\n",
+    "\n",
+    "- how to measure accuracy ?\n",
+    "\n",
+    "    - confusion matrix\n",
+    "    - accurarcy\n",
+    "    - pitfalls for unbalanced data sets\n",
+    "        e.g. diagnose HIV\n",
+    "    - precision / recall\n",
+    "\n",
+    "#### Coding session\n",
+    "\n",
+    "- Evaluate accuracy of linear beer classifier from latest section\n",
+    "- Determine precision / recall\n",
+    "\n",
+    "\n",
+    "### Part 4: underfitting/overfitting\n",
+    "\n",
+    "classifiers / regressors have parameters / degrees of freedom.\n",
+    "\n",
+    "- underfitting: linear classifier on nonlinear problem\n",
+    "\n",
+    "- overfitting:\n",
+    "\n",
+    "  - features have actual noise, or not enough information: orchid example in 2d. elevate to 3d using another feature.\n",
+    "  - polynome of degree 5 to fit points on a line + noise\n",
+    "  - points in a circle: draw very exact boundary line\n",
+    "\n",
+    "- how to check underfitting / overfitting ?\n",
+    "\n",
+    "  - measure accuracy or other metric on test dataset\n",
+    "  - cross validation\n",
+    "\n",
+    "\n",
+    "#### Coding session:\n",
+    "\n",
+    "- How to do cross validation with scikit-learn\n",
+    "- run cross validation on classifier for beer data\n",
+    "\n",
+    "\n",
+    "### Part 5: pipelines / parameter tuning with scikit-learn\n",
+    "\n",
+    "- Scikit learn API incl. summary of what we have seen up to now.\n",
+    "- pipelines, preprocessing (scaler, PCA)\n",
+    "- cross validation\n",
+    "- Hyper parameter tuning: grid search / random search.\n",
+    "\n",
+    "#### Coding session\n",
+    "\n",
+    "- examples\n",
+    "\n",
+    "\n",
+    "## DAY 2\n",
+    "\n",
+    "### Part 6: Overview classifiers\n",
+    "\n",
+    "- Nearest neighbours\n",
+    "- SVMs\n",
+    "  - demo for RBF: different parameters influence on decision line\n",
+    "- Random forests\n",
+    "- Gradient Tree Boosting\n",
+    "\n",
+    "\n",
+    "#### Coding session\n",
+    "\n",
+    "- Prepare examples for 2d classification problems incl. visualization of different\n",
+    "  decision surfaces.\n",
+    "\n",
+    "- Play with different classifiers on beer data\n",
+    "\n",
+    "### Part 7: Regression\n",
+    "\n",
+    "- What are differences compared to classification: output, how to measure accuracy, ...\n",
+    "\n",
+    "- Example: fit polynomial, examples for underfitting and overfitting\n",
+    "\n",
+    "\n",
+    "#### Coding session\n",
+    "\n",
+    "Introduce movie data set, learn SVR or other regressor on this data set.\n",
+    "\n",
+    "\n",
+    "### Part 8: Introduction neural networks\n",
+    "\n",
+    "\n",
+    "- Overview of the field\n",
+    "- Introduction to feed forward neural networks\n",
+    "- Demo Keras\n",
+    "\n",
+    "#### Coding Session\n",
+    "\n",
+    "- keras reuse network and play with it.\n",
+    "\n",
+    "\n",
+    "## Workshop\n",
+    "\n",
+    "- assist to setup the workshop material on own computer.\n",
+    "- provide example problems if attendees don't bring own data.\n",
+    "\n"
+   ]
+  }
+ ],
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+   "language": "python",
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