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Launch 8
Machine learning model that can sort images
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What is machine learning?Lecture1.1
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How do machines learn?Lecture1.2
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[LAB] Building a model in Scratch3Lecture1.3
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[LAB] How to use (step by step)Lecture1.4
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Extension – refining and adapting modelLecture1.5
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Facial recognitionLecture1.6
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Launch 5 questionsQuiz1.1
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What next?Lecture1.7
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Natural Language Processing 7
Machine learning model that can recognise natural language commands
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Natural Language ProcessingLecture2.1
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ChatbotsLecture2.2
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[LAB] Build an anti-bullying AI prototypeLecture2.3
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Test and improve your modelLecture2.4
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Chatbot applicationsLecture2.5
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Natural Language Processing 1 questionQuiz2.1
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What next?Lecture2.6
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Recommendation Systems 6
Machine learning model that can recommend the reading age of a book based on data about the book
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Recommendation systemsLecture3.1
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Using a recommendation systemLecture3.2
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Building a recommendation systemLecture3.3
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Filter bubblesLecture3.4
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Deeper dive (extension)Lecture3.5
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Recommendation Systems 3 questionsQuiz3.1
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Decisions and Ethics 4
Presentation or report summarising key points
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Self driving carsLecture4.1
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Bias in Machine LearningLecture4.2
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Review of Machine LearningLecture4.3
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Extension – Dangers of Machine LearningLecture4.4
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Machine Learning Algorithms 12
In this session we will be looking at some of the algorithms that make machine learning possible.
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IntroductionLecture5.1
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Supervised and unsupervised learningLecture5.2
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Linear RegressionLecture5.3
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[LAB] Linear regressionLecture5.4
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Naive Bayes AlgorithmLecture5.5
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[LAB] Naive BayesLecture5.6
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Decision TreeLecture5.7
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[LAB] Decision TreeLecture5.8
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k-Means ClusteringLecture5.9
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[LAB] k-Means ClusteringLecture5.10
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Neural networksLecture5.11
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SummaryLecture5.12
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(Optional) Python and Orange 3
Data visualiastions using Orange Python code for importing data and running machine learning algorithms (decision trees and kNN)
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Machine learning toolsLecture6.1
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Hello worldLecture6.2
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Decision trees – iris datasetLecture6.3
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Summary
We have covered a number of key concepts in this session.
- Explain, in your own words, the difference between supervised and unsupervised learning.
- Give an example of when you could use the the following algorithms (not examples covered in the session):
Linear regression | Naive Bayes | Decision tree - What does the k in k-means represent?
- Explain, in your own words, how a neural network learns.
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Neural networks