Machine learning is a broad topic, with a wide range of applications in scientific research.
In this series of lectures, we will look at the fundamental concepts of unsupervised and supervised learning, including the training, testing and evaluation of models for classification and regression. We also explore the basic theory of neural networks and discuss their applications to deep learning.
Examples will be provided using the Orange data science environment. No programming experience is required.
After attending this workshop, you will be better able to:
- Explain the difference between supervised and unsupervised learning.
- Select a suitable machine learning method for a given application.
- Prepare your own training and testing data sets.
- Evaluate the performance of a machine learning experiment.
Please ensure that you have Orange 3 installed and working before the first session.
The Anaconda data science platform includes Orange 3, and may be the easiest way to install: https://www.anaconda.com/ .
Alternatively, you can install Orange 3 from https://orange.biolab.si/download .
To check that everything is working properly, please follow the steps in this first tutorial video: Getting Started with Orange 01: Welcome to Orange .
Müller AC & Guido S, Introduction to Machine Learning with Python
Burger SV, Introduction to Machine Learning With R
Nielsen M, Neural Networks and Deep Learning
Tutorials and examples at scikit-learn and Kaggle
This course will give you an initial overview of the key concepts in machine learning, and should help you to get started with your own projects.
If you want to get deeper into the theory and practice of machine learning, Imperial has a number of online courses that are available for free on Coursera.
Click on "Enroll for free", then "Audit the course" at the bottom of the pop-up window.
- Getting started with TensorFlow 2
- Customising your models with TensorFlow 2
- Probabilistic Deep Learning with TensorFlow 2
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