Introduction to Machine Learning

Machine learning is a broad topic, with a wide range of applications in scientific research. 

In this series of lectures, we will introduce 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 Orange data science environment.  No prior experience of programming is required.

Syllabus:

• Unsupervised learning
  • Principal Component Analysis
  • Clustering
• Supervised learning
  • Linear regression
  • Logistic regression
  • Decision trees
• Evaluating performance
  • Cross-validation
  • ROC curves
• Improving performance
  • Feature selection
  • Ensemble methods
• Artificial neural networks
  • Multi-layer perceptron
  • Deep learning applications

This course is open to Research Degree Students, Postdocs & Research Fellows. Limited spaces available for wider Imperial community.

Learning Outcomes:

After completing 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.

Prerequisites:

No prior experience of programming is required.

How to book

• Early Career Researchers (Research Degree Students, Postdocs, Research Fellows) should book via Inkpath using your Imperial Single-Sign-On.
• All other members of the Imperial community, should book here.

Please ensure you have read and understood ECRI’s cancellation policy before booking.