Experimental Design for Machine Learning
Explore experimental design for machine learning in plant phenotyping, enhancing data collection and analysis.
Duration
5 weeksWeekly study
1 hour100% online
How it works
Experimental Design for Machine Learning
Develop innovative strategies for machine learning with experimental design
Embark on a focused exploration into the core methodologies of experimental design tailored for machine learning, particularly within the context of plant phenotyping.
This course is designed to bridge the gap between theoretical knowledge and practical application, providing a comprehensive understanding of how experimental design principles can optimise machine learning outcomes.
Apply experimental design techniques in machine learning
Explore the foundational aspects of experimental design as it applies to machine learning. Understand the critical components of setting up experiments, from hypothesis formation to variable control and data analysis, which are crucial for achieving reliable results.
Investigate various experimental designs used in real-world machine learning scenarios, focusing on their applications in improving model reliability and performance.
Develop strategies for data collection and analysis for plant phenotyping
Delve into the strategies for effective data collection and annotation essential for training robust machine learning models.
Learn how to expand and refine datasets to cover a broad range of variables and conditions that will enhance the predictive power of your models.
Utilise techniques for model selection and performance
Sift through and select appropriate machine learning models and adjust parameters to maximise performance.
Discuss case studies demonstrating the successful application of these techniques in plant phenotyping.
By the end of this course, you’ll have a deep understanding of how experimental design supports machine learning, driving innovation in biosciences.
Syllabus
Week 1
Experimental design
Welcome to the course
An overview of what's in store over the five weeks of the course
Data collection and annotation
What are the things we need to look out for when collecting image data, and how can we add information to our images to help train machine learning algorithms?
Summary and review
Reflecting on what you have learned in Week 1, including a short quiz.
Week 2
Understanding and working with data
Organising your datasets
What do we need to consider in order to keep datasets organised? And how do we split data within datasets?
Expanding your dataset
Tips on how you can expand the number of images in your training dataset, via augmentation, use of synthetic data, or other pre-existing datasets
Releasing data
Why might you want to consider releasing your data?
Summary and review
A review of week 1 of the course, plus a quiz
Week 3
Choosing and using models
Software, model selection, and training
What to consider when choosing computing platforms and models for your machine learning projects, plus a look at training and inference.
Improving performance
What can we do to improve model performance?
Summary and Review
A review of Week 3: Choosing and using models, including a quiz
Week 4
Trusting results
Trusting results
How well can we trust the results of deep learning and machine learning models?
Interpreting output
How should we interpret the ouput from deep learning and machine learning?
Improving results
Can we improve the results of our models?
Summary and review
A summary and review of Week 4 - Trusting results, with a quiz
Week 5
Practical tips and tricks
Tips and tricks
What to consider in terms of computer software and hardware, some tips on good practice when writing papers containing applications of machine learning and deep learning, and some thoughts on interdiscipinary communication.
Course summary
A wrap up of the course, with links to a practical exercise
When would you like to start?
Start straight away and join a global classroom of learners. If the course hasn’t started yet you’ll see the future date listed below.
Available now
Learning on this course
On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.
What will you achieve?
By the end of the course, you‘ll be able to...
- Design experiments that will collect good quality data for use in machine learning and deep learning models
- Improve and organise datasets collected experimentally for use in machine learning and deep learning models
- Compare and select machine learning and deep learning models for use with your experimental data
- Interpret and evaluate results from machine learning and deep learning models and discuss ethical considerations surrounding their use
- Report effectively on findings from machine learning and deep learning models trained on experimental data
Who is the course for?
This course is designed for bioscience professionals, particularly those in plant phenotyping, looking to enhance their skills in experimental design and machine learning to improve data collection, analysis, and model implementation.
What software or tools do you need?
No specific software is required. One demonstration will use Python.
Who will you learn with?
Michael Pound is a Research Fellow and Lecturer in the School of Computer Science, University of Nottingham, UK. My research interests focus on Deep learning applied to plant phenotyping problems.
Andrew French is a Professor of Computer Science at the University of Nottingham. His area of research is developing novel image analysis methods, specifically for biological images.
Nathan Mellor is a Post-Doc at the University of Nottingham. His research background is mathematical models of plants and plant tissues, using a range of programming and image analysis methods.
Who developed the course?
The University of Nottingham
The University of Nottingham is committed to providing a truly international education, inspiring students with world-leading research and benefitting communities all around the world.
Ways to learn | Buy this course | Subscribe & save | Limited access |
|---|---|---|---|
| Choose the best way to learn for you! | $134/one-off payment | $244.99 for a whole year Automatically renews | Free |
| Fulfill your current learning need | Develop skills to further your career | Sample the course materials | |
| Access to this course | tick | tick | Access expires 14 Oct 2024 |
| Access to 1,000+ courses | cross | tick | cross |
| Learn at your own pace | tick | tick | cross |
| Discuss your learning in comments | tick | tick | tick |
| Certificate when you're eligible | Printed and digital | Digital only | cross |
Cancel for free anytime |
Ways to learn
Choose the best way to learn for you!
Subscribe & save
$244.99 for a whole year
Automatically renews
Develop skills to further your career
- Access to this course
- Access to 1,000+ courses
- Learn at your own pace
- Discuss your learning in comments
- Digital certificate when you're eligible
Cancel for free anytime
Buy this course
$134/one-off payment
Fulfill your current learning need
- Access to this course
- Learn at your own pace
- Discuss your learning in comments
- Printed and digital certificate when you’re eligible
Limited access
Free
Sample the course materials
- Access expires 14 Oct 2024
Find out more about certificates, Unlimited or buying a course (Upgrades) Sale price available until 31 October 2024 at 23:59 (UTC). T&Cs apply. | |||
Find out more about certificates, Unlimited or buying a course (Upgrades)
Sale price available until 31 October 2024 at 23:59 (UTC). T&Cs apply.
Learning on FutureLearn
Your learning, your rules
- Courses are split into weeks, activities, and steps to help you keep track of your learning
- Learn through a mix of bite-sized videos, long- and short-form articles, audio, and practical activities
- Stay motivated by using the Progress page to keep track of your step completion and assessment scores
Join a global classroom
- Experience the power of social learning, and get inspired by an international network of learners
- Share ideas with your peers and course educators on every step of the course
- Join the conversation by reading, @ing, liking, bookmarking, and replying to comments from others
Map your progress
- As you work through the course, use notifications and the Progress page to guide your learning
- Whenever you’re ready, mark each step as complete, you’re in control
- Complete 90% of course steps and all of the assessments to earn your certificate
Want to know more about learning on FutureLearn? Using FutureLearn
Do you know someone who'd love this course? Tell them about it...
More courses you might like
Learners who joined this course have also enjoyed these courses.
Browse more in IT & Computer Science and Nature & Environment
