Machine learning approaches

What the syllabus asks of you

• A4.3.1 Explain how linear regression is used to predict continuous outcomes.
• A4.3.2 Explain how classifications techniques in supervised learning are used to predict discrete categorical outcomes.
• A4.3.3 Explain the role of hyperparameter tuning when evaluating supervised learning algorithms.
• A4.3.4 Describe how clustering techniques in unsupervised learning are used to group data based on similarities in features.
• A4.3.5 Describe how learning techniques using the association rule are used to uncover relations between different attributes in large data sets.
• A4.3.6 Describe how an agent learns to make decisions by interacting with its environment in reinforcement learning.
• A4.3.7 Describe the application of genetic algorithms in various real-world situations.
• A4.3.8 Outline the structure and function of ANNs and how multi-layer networks are used to model complex patterns in data sets.
• A4.3.9 Describe how CNNs are designed to adaptively learn spatial hierarchies of features in images.
• A4.3.10 Explain the importance of model selection and comparison in machine learning.