AI BootCamp (Module 1 Crash Course)
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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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Introduction
So far we have been using machine learning models without needing to understand how they work. We fed data into the model and the results came out the other end. What happened in the middle was a mystery.
In this session we will be looking at some of the algorithms that make machine learning possible. (An algorithm is a list of rules to follow in order to solve a problem.)