The University of Nottingham
- Start Date: 12 Aug, 2024
- 5 weeks
- Study Content: Videos
Explore experimental design for machine learning in plant phenotyping, enhancing data collection and analysis.
Course Fee: Free
Certificate Cost: See Fees and Eligibility
Course Description
This course is made available through the eLearnAfrica and FutureLearn partnership.
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.
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.
No specific software is required. One demonstration will use Python.
Certificate cost may vary. You will be redirected to the host page for cost and payment options.
The University of Nottingham
The University of Nottingham is committed to providing a truly international education, inspiring students, producing world-leading research and benefiting the communities around campuses in the UK, China and Malaysia. The purpose of the University is to improve life for individuals and societies worldwide. By bold innovation and excellence in all that it does, the University makes both knowledge and discoveries matter.
The University of Nottingham has 42,000 students at award-winning campuses in the United Kingdom, China and Malaysia. It was ‘one of the first to embrace a truly international approach to higher education’, according to the Sunday Times University Guide 2013. It is also one of the most popular universities among graduate employers, one of the world’s greenest universities, and winner of the Times Higher Education Award for ‘Outstanding Contribution to Sustainable Development’. It is ranked in the UK’s Top 10 and the World’s Top 75 universities by the Shanghai Jiao Tong and the QS World Rankings.
More than 90 per cent of research at The University of Nottingham is of international quality, according to the most recent Research Assessment Exercise. The University aims to be recognised around the world for its signature contributions, especially in global food security, energy & sustainability, and health. It won a Queen’s Anniversary Prize for Higher and Further Education for its research into global food security.
This institution is available on eLearnAfrica through partnership with FutureLearn.
COLLABORATION POLICY
By enrolling in a course on or through eLearnAfrica, you are joining a special worldwide community of learners. The aspiration of eLearnAfrica is to provide anyone with an internet connection access to courses from the best universities and institutions in the world and to provide our learners the best educational experience internet technology enables. You are a part of the community that will help eLearnAfrica achieve this goal. eLearnAfrica depends upon your motivation to learn the material and to do so with honesty and academic integrity. In order to participate in eLearnAfrica, you must agree to the Honor Code below and any additional terms specific to a class.
ELEARNAFRICA HONOR CODE PLEDGE
By enrolling in a course, program, or degree hosted on the eLearnAfrica App or Site, I agree that I will:
-
Complete all tests and assignments on my own, unless collaboration on an assignment is explicitly permitted.
-
Maintain only one user account and not let anyone else use my username and/or password.
-
Not engage in any activity that would dishonestly improve my results, or improve or hurt the results of others.
-
Not post answers to problems that are being used to assess student performance.
-
Abide by any and all requirements of the eLearnAfrica Participants as may regard the expectations of civil or academic behavior or of community standards.
VIOLATIONS
If you are found in violation of the Terms and Conditions or Honor Code, you may be subject to one or more of the following actions:
-
Receiving a zero or no credit for an assignment;
-
Having any certificate earned in the course withheld or revoked;
-
Being unenrolled from a course, learning program or degree program; or
-
Termination of your use of the App and/or Site.
-
Additional actions may be taken at the sole discretion of eLearnAfrica and eLearnAfrica course providers.
No refunds will be issued in the case of any corrective action for such violations.
Honor code violations will be determined at the sole discretion of eLearnAfrica, the Partners, or Members. You will be notified if a determination has been made that you have violated this honor code and you will be informed of the corresponding action to be taken as a result of the violation.
CHANGING THE HONOR CODE
Please note that we review and may make changes to this Honor Code from time to time. Any changes to this Honor Code will be effective immediately upon posting on this page, with an updated effective date. By accessing the App and/or Site after any changes have been made, you signify your agreement on a prospective basis to the modified Honor Code and any changes contained therein. Be sure to return to this page periodically to ensure familiarity with the most current version of this Honor Code.
Effective Date: September 22, 2016