• University of Nottingham
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Machine Learning for Image Data

Master the principles and applications of machine learning for image data to harness its potential for plant phenotyping.

677 enrolled on this course

Image of a red poppy in a field of corn.

Machine Learning for Image Data

677 enrolled on this course

  • 5 weeks

  • 3 hours per week

  • Digital certificate when eligible

  • Intermediate level

Find out more about how to join this course

  • Duration

    5 weeks
  • Weekly study

    3 hours
  • 100% online

    How it works
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    $244.99 for a whole yearLearn more

Become an expert in machine learning for bioscience

Machine learning has made it possible to process vast quantities of image data. That means it can enhance and facilitate the work of bioscience researchers, particularly the field of plant phenotyping.

On this five-week course from the University of Nottingham, you’ll gain an overview of the applications of machine learning for image data, focusing specifically on its use in plant phenotyping.

Gain an overview of machine learning as it applies to biological image data

You’ll start the course with an overview of machine learning, and an introduction to image data and features.

You’ll gain the background you need to understand and apply machine learning in your own bioscience research.

Master common techniques and softwares for image analysis

Once you’ve mastered the principles of machine learning for image data, you’ll start building the practical skills you need to navigate machine learning software.

Weeks 3 and 4 of the course will cover the main techniques for processing image data, some common challenges surrounding these, and useful tips and tricks to help you overcome them.

Whether you want to model data through a decision tree or create visualisations using Python, you’ll gain the hands-on experience you need for your research.

Understand neural networks and deep learning

In your last week of the course, you’ll look more closely at a specific subfield of machine learning: deep learning. You’ll learn how neural networks can be used to process biological images in the same way the human brain would.

By the end of the course, you’ll have an understanding of how machine learning can be used with biological image data, and the skills you need to harness it in your own bioscience research.

Syllabus

  • Week 1

    What is machine learning?

    • A root system on a blue background with the root tips highlighted by pink circles

      Introduction to machine learning

      An introduction to the course, and an introduction to image data and features.

    • An image of a tomato with the location idicated with a bounding box and labelled as a tomato

      Example machine learning problems

      A discussion of the main types of problems you might solve with machine learning, and the kinds of problems that are specific to image data. We also look at classification and clustering applications with a set of example data.

    • A sketch of a table of data showing a length measurement in one column and a data label of plant type in the other column

      Supervised versus unsupervised learning

      To tackle any problem using machine learning you need to establish whether supervised or unsupervised learning is appropriate to your dataset. This activity explains the difference.

    • A scatter plot showing the fit of a linear regression model to some data

      Common tasks - classification and regression

      An overview of the common machine learning tasks classification and regression, plus a look at some other frequently used terminology.

    • A montage of software generated plots, source code and terminal output, demonstating some of the software used in the course

      Software tools

      An overview to the software packages used in the course, including Scikit-Learn, Matplotlib, and Pandas.

    • A set of axes labelled petal width against sepal length with a scatter plot of dots. The dots are in three colours, and roughly arranged in clusters. The axes background is also coloured in three bands of colour, roughly overlapping the clusters.

      Summary and review

      Summary and review of week 1, with a quiz and practical activity.

  • Week 2

    Data and features

    • Two grids, each labelled 1,2,3 on the top row. Cell one has a one in the first grid, cell two has a one in the second grid. Otherwise all cells are zero.ll

      Types of data and features

      An overview of the types of data used in machine learning, and an introduction to features and feature extraction.

    • A sketch of a stick figure divided in squares, with one square enlarged an the angles of lines counted below

      Feature extraction

      A look at feature extraction for use in machine learning. A particular focus on feature extraction from image data.

    • A screenshot of using the Fiji software to outline a tomato in an image of some tomatoes on a vine

      Labelling image data

      Image data often needs to be labelled or annotated for use in machine learning models. This activity goes over why and how you might annotate image data, and introduces some software tools.

    • A spreadsheet containing labelled columns of data. One piece of data is missing, highlighted, and containing the text NaN

      Pre-processing data

      How to deal with noisy and incomplete data, and a look at pre-processing of image data.

    • An image of a daffodil flower on the left, with a visualisation of HOG extracted features on the right

      Summary and review

      A review of the week's content, with a practical and a quiz.

  • Week 3

    Common techniques

    • A screenshot of Andrew French from the introductory video

      Introduction

      In week 3 we will look in more detail at some common machine learning methods for clustering, classification, and regression. Plus a look at methods for model evaluation, visualisation, and selection.

    • A plot displaying a mixture of Gaussian distributions in different colours, with a combined distribution plot overlain.

      Clustering

      A closer look at clustering methods, in particular K-means clustering.

    • A diagram of an example decision tree, showing the decisions as linked boxes

      Classification

      A closer look at the common classification methods of Decision Trees and Naive Bayes.

    • A scatter plot showing some data, and a curved linear regression line, fitted using quadratic coefficients

      Regression

      A look at regression techniques, in particular linear regression.

    • Two confusion matrices side by side showing the results of a KNN classification, one with K=1 and one with K=2.

      Evaluation, visualisation, and selection

      How to evaluate your machine learning models. Includes accuracy, precision, recall, and F scores. Plus a look at ways to visualise your results, including confusion matrices, and some advice on model selection.

    • A confusion matrix for a classifier of four flower species

      Summary and review

      A review of the week's content, with a practical and a quiz.

  • Week 4

    Tips and tricks

    • A screenshot of Andrew French from the introductory video

      Introduction

      An introduction to Week 4, Tips and Tricks.

    • Training plot

      Good Training Practice

      A look at choice of features, using learning performance curves to improve model training, splitting datasets and use of cross-validation.

    • Cross validation

      Data augmentation

      A look at methods to artifically increase the size of datasets by using data augmentation.

    • overfitting

      Common challenges

      Including overfitting, regularisation, the "Curse of Dimensionality", and class imbalance.

    • Curse of dimensionality

      Summary and review

      A review of the week's content, with a quiz and practical activity.

  • Week 5

    Deep learning

    • A diagram showing an image of a root leading to HOG features, then an array of numbers, then a neural network model, then an output identifying it as a root.

      What is deep learning?

      A look at what deep learning is and how it compares with the machine learning learning methods we have considered previously in the course.

    • Andrew French with a whiteboard diagram of a neural network

      Neural networks and deep learning

      An overview of how deep learning systems are constructed. Starting with perceptrons, then neural networks, and finally convolutional neural networks.

    • A screenshot of some example deep learning code in Colab

      Some simple tools

      We won't be doing any practical deep learning within this course. But to give you a taste of how we will cover it in future units, we introduce Python notebooks and Colab, and provide some links for further reading.

    • A diagram of a convolutional neural network architecture used to detect wheat leaf tips

      Summary and review

      A review of the week and course's content, with a quiz

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...

  • Explain what machine learning is, and how it relates to image data
  • Perform simple image labelling and pre-processing tasks
  • Classify supervised, unsupervised and semi-supervised machine learning techniques
  • Code some simple machine learning scripts using Python Scikit-Learn
  • Describe some common machine learning tasks, such as clustering, regression, and classification
  • Investigate deep learning, and how it differs from machine learning

Who is the course for?

This course is designed for researchers and other professionals working in plant phenotyping or related bioscience disciplines, who want to know more about how machine learning can be used with image data.

What software or tools do you need?

Any software needed for the course is available to download for free and introduced as part of the course content.

Who will you learn with?

Andrew French

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.

Simon Parsons

I have been a researcher in artificial intelligence since 1989, when I started my PhD. In 2019, I joined the University of Lincoln to work on the application of AI and robotics to agriculture.

Who developed the course?

University of Nottingham

The University of Nottingham

The University of Nottingham is a research-focused campus university, described as “the nearest thing Britain has to a truly global university” by The Times. As a member of both the Russell Group and Universitas 21 international network, our reach extends across our campuses in the UK, China and Malaysia, with a diverse student body drawn from over 150 countries.

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University of Lincoln

The University of Lincoln is proud to be ranked as a Top 3 University in the WhatUni University of the Year 2024 awards, as well as a triple-gold institution in the latest Teaching Excellence Framework (TEF) 2023.

  • Established

    1996
  • Location

    Lincoln, Lincolnshire, UK

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  • Discuss your learning in comments
  • Digital certificate when you're eligible

Cancel for free anytime

Buy this course

$109/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 19 Mar 2025

Find out more about certificates, Unlimited or buying a course (Upgrades)

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Sale price available until 3 March 2025 at 23:59 (UTC). T&Cs apply.

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