diff --git a/05_classifiers_overview.ipynb b/05_classifiers_overview.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..19d31bf1ad147ed9e2f28dd2b7d7f8739c62e6c5
--- /dev/null
+++ b/05_classifiers_overview.ipynb
@@ -0,0 +1,39 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Chapter 5: An overview of classifiers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "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": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/05_preprocessing_pipelines_and_hyperparameter_optimization.ipynb b/06_preprocessing_pipelines_and_hyperparameter_optimization.ipynb
similarity index 98%
rename from 05_preprocessing_pipelines_and_hyperparameter_optimization.ipynb
rename to 06_preprocessing_pipelines_and_hyperparameter_optimization.ipynb
index de86c6fb8f08b01236ebfac58a9acbd57d38101c..15f0dfc051b99ad0ae18ebd802eccede6735edfc 100644
--- a/05_preprocessing_pipelines_and_hyperparameter_optimization.ipynb
+++ b/06_preprocessing_pipelines_and_hyperparameter_optimization.ipynb
@@ -1,5 +1,12 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Chapter 6: Preprocessing pipelines and hyperparameters optmization"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -341,7 +348,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.6"
+ "version": "3.7.2"
}
},
"nbformat": 4,
diff --git a/course_layout.md b/course_layout.md
index 2c6f3eaf0722fc4f8b2fd7c86b4dfe5dedbd868b..fca5da46b96562473c68690e052bfb295ad5a7c4 100644
--- a/course_layout.md
+++ b/course_layout.md
@@ -39,7 +39,7 @@ TBD: prepare coding session
## Part 1: Introduction (UWE)
- What is machine learning ?
-
+
- learning from examples
- working with hard to understand data.
- automatation
@@ -47,39 +47,39 @@ TBD: prepare coding session
- What are features / samples / feature matrix ?
- always numerical / categorical vectors
- examples: beer, movies, images, text to numerical examples
-
-- Learning problems:
-
+
+- Learning problems:
+
- unsupervised:
-
+
- find structure in set of features
- beers: find groups of beer types
-
+
- supervised:
-
+
- classification: do I like this beer ?
example: draw decision tree
-
-
+
+
## Part 2a: supervised learning: classification
Intention: demonstrate one / two simple examples of classifiers, also
introduce the concept of decision boundary
-
+
- idea of simple linear classifier: take features, produce real value ("uwes beer score"), use threshold to decide
-> simple linear classifier (linear SVM e.g.)
-> beer example with some weights
-
+
- show code example with logistic regression for beer data, show weights, plot decision function
### Coding session:
- change given code to use a linear SVM classifier
-
+
- use different data (TBD) set which can not be classified well with a linear classifier
- tell to transform data and run again (TBD: how exactly ?)
-
+
## Part 2b: supervised learning: regression (TBD: skip this ?)
@@ -130,7 +130,7 @@ Intention: accuracy is usefull but has pitfalls
- how to measure accuracy ?
- (TDB: skip ?) regression accuracy
- -
+ -
- classifier accuracy:
- confusion matrix
- accurarcy
@@ -138,7 +138,7 @@ Intention: accuracy is usefull but has pitfalls
e.g. diagnose HIV
- precision / recall
- ROC ?
-
+
- exercise: do cross val with other metrics
### Coding session
@@ -152,29 +152,7 @@ Intention: accuracy is usefull but has pitfalls
# Day 2
-
-## Part 5: pipelines / parameter tuning with scikit-learn
-
-- Scicit learn api: recall what we have seen up to now.
-- pipelines, preprocessing (scaler, PCA)
-- cross validatioon
-- parameter tuning: grid search / random search.
-
-
-### Coding session
-
-- build SVM and LinearRegression crossval pipelines for previous examples
-- use PCA in pipeline for (+) to improve performance
-- find optimal SVM parameters
-- find optimal pca components number
-
-### Coding par
-
-Planning: stop here, make time estimates.
-
-
-
-## Part 6: classifiers overview
+## Part 5: classifiers overview
Intention: quick walk throught throug reliable classifiers, give some background
idea if suitable, let them play withs some incl. modification of parameters.
@@ -188,7 +166,14 @@ diagram.
- Random forests
- Gradient Tree Boosting
-show decision surfaces of these classifiers on 2d examples.
+topics to include:
+
+- interoperability of results (in terms features importance, e.g. SVN w/ hig deg poly
+ kernel)
+- some rules of thumbs: don't use KNN classifiers for 10 or more dimensions (why? paper
+ link)
+- show decision surfaces for diff classifiers (extend exercise in sec 3 using
+ hyperparams)
### Coding session
@@ -197,10 +182,26 @@ show decision surfaces of these classifiers on 2d examples.
- MNIST example
-## Part 7: Start with neural networks. .5 day
+## Part 6: pipelines / parameter tuning with scikit-learn
+
+- Scikit-learn API: recall what we have seen up to now.
+- pipelines, preprocessing (scaler, PCA)
+- cross validation
+- parameter tuning: grid search / random search.
+
+### Coding session
+- build SVM and LinearRegression crossval pipelines for previous examples
+- use PCA in pipeline for (+) to improve performance
+- find optimal SVM parameters
+- find optimal pca components number
+
+
+## Part 7: Start with neural networks. .5 day
+## Planning
+Stop here, make time estimates.
diff --git a/index.ipynb b/index.ipynb
index eeab403a24bb7ef60c074dbb9d7fa686b146540f..2f2e67ea8cf903ce67753f3e022f49ab8a9575e5 100644
--- a/index.ipynb
+++ b/index.ipynb
@@ -1,5 +1,17 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div class=\"alert alert-block alert-danger\"><p>\n",
+ "<strong>TODOs</strong>\n",
+ "<ol>\n",
+ "<li>Write script which removes the solution proposals (cells starting with <code>#SOLUTION</code>) and creates a new notebook.</li>\n",
+ "</ol>\n",
+ "</p></div>\n"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -13,12 +25,34 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "<div class=\"alert alert-block alert-danger\"><p>\n",
- "<strong>TODOs</strong>\n",
- "<ol>\n",
- "<li>Write script which removes the solution proposals (cells starting with <code>#SOLUTION</code>) and creates a new notebook.</li>\n",
- "</ol>\n",
- "</p></div>\n"
+ "# Course: Introduction to Machine Learning with Python\n",
+ "\n",
+ "<div class=\"alert alert-block alert-warning\">\n",
+ " <p><i class=\"fa fa-warning\"></i> <strong>Goal</strong></p>\n",
+ " <p>Quickly get your hands dirty with Machine Learning and know what your doing.<p>\n",
+ "</div>\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## What will you learn?\n",
+ "\n",
+ "* Basic concepts of Machine Learning (ML).\n",
+ "* General overview of supervised learning and related methods.\n",
+ "* How to quickly start with ML using `scikit-learn` Python library."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## What will you NOT learn?\n",
+ "\n",
+ "* How to program with Python.\n",
+ "* How exactly ML methods work.\n",
+ "* Unsupervised learning methods."
]
},
{
@@ -30,6 +64,10 @@
"<ol>\n",
" <li><a href=\"01_introduction.ipynb\">Introduction</a></li>\n",
" <li><a href=\"02_classification.ipynb\">Classification</a></li>\n",
+ " <li><a href=\"03_overfitting_and_cross_validation.ipynb\">Overfitting and cross-validation</a></li>\n",
+ " <li><a href=\"04_measuring_quality_of_a_classifier.ipynb\">Metrics for evaluating the performance</a></li>\n",
+ " <li><a href=\"05_classifiers_overview.ipynb\">An overview of classifiers</a></li>\n",
+ " <li><a href=\"06_preprocessing_pipelines_and_hyperparameter_optimization.ipynb\">Preprocessing pipelines and hyperparameters optmization</a></li>\n",
" <li>...</li>\n",
" \n",
"</ol>"