Syllabus

• Week 1

  What is machine learning?

  • Introduction to machine learning

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

  • 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.

  • 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.

  • 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.

  • Software tools

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

  • Summary and review

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

• Week 2

  Data and features

  • Types of data and features

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

  • Feature extraction

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

  • 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.

  • Pre-processing data

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

  • Summary and review

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

• Week 3

  Common techniques

  • 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.

  • Clustering

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

  • Classification

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

  • Regression

    A look at regression techniques, in particular linear regression.

  • 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.

  • Summary and review

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

• Week 4

  Tips and tricks

  • Introduction

    An introduction to Week 4, Tips and Tricks.

  • Good Training Practice

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

  • Data augmentation

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

  • Common challenges

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

  • Summary and review

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

• Week 5

  Deep learning

  • 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.

  • 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.

  • 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.

  • Summary and review

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

Who will you learn with?