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Launch 8
Machine learning model that can sort images
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Lecture1.1
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Lecture1.2
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Lecture1.3
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Lecture1.4
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Lecture1.5
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Lecture1.6
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Quiz1.1
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Lecture1.7
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Natural Language Processing 7
Machine learning model that can recognise natural language commands
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Lecture2.1
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Lecture2.2
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Lecture2.3
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Lecture2.4
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Lecture2.5
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Quiz2.1
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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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Lecture3.1
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Lecture3.2
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Lecture3.3
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Lecture3.4
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Lecture3.5
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Quiz3.1
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Decisions and Ethics 4
Presentation or report summarising key points
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Lecture4.1
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Lecture4.2
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Lecture4.3
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Lecture4.4
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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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Lecture5.1
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Lecture5.2
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Lecture5.3
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Lecture5.4
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Lecture5.5
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Lecture5.6
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Lecture5.7
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Lecture5.8
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Lecture5.9
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Lecture5.10
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Lecture5.11
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Lecture5.12
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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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Lecture6.1
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Lecture6.2
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Lecture6.3
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[LAB] Decision Tree
You are going to create a decision tree to enable you to classify animals based on various features such as whether they have feathers or produce milk for their young.
- Use your workbook to draw out your decision tree. It may be better to do this in pencil in case you make a mistake.
- Open Session 5 data and click on the Animals tab. This shows different features for a range of animals. Your task is to produce a decision tree to separate out the different types of animal, e.g. mammals, birds, fish.
- To make this task easier sort the table on type (Data > Sort > Type).
- To start your decision tree look at mammals – Can you find a feature that mammals have and no other type of animal has? This will be the first node on your decision tree.
- Next, have a look a look at another type – can you spot a feature that only that type has? (It might help to hide the rows containing mammals – highlight these rows and click on Format > Hide/Unhide).
- Continue analysing the data until you have split out all the different types of animal – for some decisions you may need a combination of features to split out two types.
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Decision Tree
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k-Means Clustering