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

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.

Dates & Booking Information

• Monday 04 November 2024 (Part 1), Tuesday 05 November 2024 (Part 2) & Wednesday 06 November 2024 (Part 3), 10:00-12:00, Microsoft Teams
  
  
• Monday 20 January 2025 (Part 1), Tuesday 21 January 2025 (Part 2) & Wednesday 22 January 2025 (Part 3), 14:00-16:00, Microsoft Teams
  
  
• Tuesday 29 April 2025 (Part 1), Wednesday 30 April 2025 (Part 2) & Thursday 01 May 2025 (Part 3), 10:00-12:00, Microsoft Teams
  
  
• Monday 23 June 2025 (Part 1), Tuesday 24 June 2025 (Part 2) & Wednesday 25 June 2025 (Part 3), 14:00-16:00, Microsoft Teams
  
  

To book your place, please follow the booking process advertised on the main programme page