From 453040d761368fde3ac965dcffc2a4bff2cfd83a Mon Sep 17 00:00:00 2001
From: Uwe Schmitt <uwe.schmitt@id.ethz.ch>
Date: Mon, 24 Sep 2018 21:08:27 +0200
Subject: [PATCH] reformatted proposal
---
.gitignore | 2 +
content.md | 161 -------------------
machine_learning_workshop_proposal.ipynb | 195 +++++++++++++++++++++++
3 files changed, 197 insertions(+), 161 deletions(-)
create mode 100644 .gitignore
delete mode 100644 content.md
create mode 100644 machine_learning_workshop_proposal.ipynb
diff --git a/.gitignore b/.gitignore
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+.ipynb_checkpoints/
+venv*
diff --git a/content.md b/content.md
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+++ /dev/null
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-# 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.
-
-
diff --git a/machine_learning_workshop_proposal.ipynb b/machine_learning_workshop_proposal.ipynb
new file mode 100644
index 0000000..defa9c2
--- /dev/null
+++ b/machine_learning_workshop_proposal.ipynb
@@ -0,0 +1,195 @@
+{
+ "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"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
--
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