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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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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