Introduction to Machine Learning

Introduction to Machine Learning#

Course description#

This course is designed to introduce core machine learning concepts. Students will explore the machine learning landscape, and the main stages of the machine learning pipeline. They will learn about model selection, error, evaluation and validation, and put all of these into practice using Python. Scikit-learn will be introduced, and used to pre-process data, train models, and build pipelines.

Course objectives#

Explain the main theoretical concepts of machine learning (at a high level), the machine learning landscape, and be able to provide some examples of machine learning applications.
Train basic linear models using scikit-learn, building understanding of model-based learning.
Understand concepts such as model selection, error, evaluation, validation, and be able to put these into practice with Python.
Explain the main stages of the machine learning pipeline, and be able to create a pipeline running on real-world data.

Pre-requisite knowledge#

Attendees should have taken the Introduction to Python course described here, and Python for Data Analysis described here.

Installation guide#

All CfRR courses require attendees to use their own computer/laptop to follow workshop activities and take effective notes.

As this course extends upon Introduction to Python and Python for Data Analysis, the installation instructions are the same, available here. The main packages used are Numpy, Pandas, Matplotlib, Scikit-learn, and Plotly.

Self study materials#

This course will be delivered via a mixture of slides and programming tutorials. All materials for this course can be found and downloaded from the course landing page here.

Developers#

The developer of this course is Simon Kirby.

License info#

Most materials are licensed under MIT. The machine learning pipeline example is licensed under Apache 2.0.

Instructional Material

The instructional material in this course is copyright © 2024 University of Exeter and is made available under the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/). Instructional material consists of material that is contained within the “individual_modules/introduction_to_machine_learning” directory, and images folders in this directory, with the exception of code snippets and example programs found in files within these folders. Such code snippets and example programs are considered software for the purposes of this license.

Softwares

Except where otherwise noted, software provided in this repository is made available under the MIT license (https://opensource.org/licenses/MIT).

The example in the machine learning pipeline task was adapted from code available under the Apache 2.0 license,

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.