Machine Learning for beginners
Applying AI to biology
Machine learning is an exciting set of tools and techniques for classifying and analysing data. This course will introduce the state of the art in machine learning and provide some hand-on experience with data handling and analysis.
Instructors
- Matthew Hartley
Audience
Those interested in applying machine learning approaches to their work.
Overview
Artificial intelligence is an area rich with possibilities. Machine learning (ML) is a major subfield of AI, that provides a powerful set of tools and approaches to solving problems in data analysis and classification. These approaches have proved successful across a range of biological domains from protein homology prediction, through sequence analysis to image based phenotying.
In this short course, we'll provide a beginner's guide to Machine Learning. This will involve understanding what state of the art is in the field, how to recognise when ML approaches are likely to be useful, how to prepare data for analysis and how to apply various techniques. The course will involve a mixture of talks and hands-on material.
Schedule
Day1
9:30 Start
11:00 Coffee break
12:30 Lunch
13:30 Afternoon session
15:00 Coffee break
16:30 End
Learning outcomes
By the end of the course you should be able to:
- Understand machine learning terminology
- Identify problem domains where machine learning will be useful
- Carry out some basic techniques for data cleaning and visualisation
- Apply machine learning techniques to sample data
Pre-requisites
None
Full course syllabus
- Introduction - what we'll cover
- What is machine learning?
- Practical - A hands-on introduction
- Obtaining, handling and visualising data
- Practical - Cleaning and visualising data
- The machine learning algorithm zoo
- Practical - Clustering with Machine Learning
- AI and Deep Learning
- Practical - Classifying images with Deep Learning
Course materials
Course book
Applying AI to biology
Instructors
- Matthew Hartley
Audience
Those interested in applying machine learning approaches to their work.Overview
Artificial intelligence is an area rich with possibilities. Machine learning (ML) is a major subfield of AI, that provides a powerful set of tools and approaches to solving problems in data analysis and classification. These approaches have proved successful across a range of biological domains from protein homology prediction, through sequence analysis to image based phenotying. In this short course, we'll provide a beginner's guide to Machine Learning. This will involve understanding what state of the art is in the field, how to recognise when ML approaches are likely to be useful, how to prepare data for analysis and how to apply various techniques. The course will involve a mixture of talks and hands-on material.Schedule
Day1
| 9:30 | Start |
| 11:00 | Coffee break |
| 12:30 | Lunch |
| 13:30 | Afternoon session |
| 15:00 | Coffee break |
| 16:30 | End |
Learning outcomes
By the end of the course you should be able to:- Understand machine learning terminology
- Identify problem domains where machine learning will be useful
- Carry out some basic techniques for data cleaning and visualisation
- Apply machine learning techniques to sample data
Pre-requisites
NoneFull course syllabus
- Introduction - what we'll cover
- What is machine learning?
- Practical - A hands-on introduction
- Obtaining, handling and visualising data
- Practical - Cleaning and visualising data
- The machine learning algorithm zoo
- Practical - Clustering with Machine Learning
- AI and Deep Learning
- Practical - Classifying images with Deep Learning