Unit 06: Machine Learning #

You can find the Machine Learning slides HERE. It has SL and HL content.

A4.1.1 Describe the types of machine learning and their applications in the real world.
A4.1.2 Describe the hardware requirements for various scenarios where machine learning is deployed.
A4.4.1 Discuss the ethical implications of machine learning in real-world scenarios.
A4.4.2 Discuss ethical aspects of the increasing integration of computer technologies into daily life.

A4.2.1 Describe the significance of data cleaning.
A4.2.2 Describe the role of feature selection.
A4.2.3 Describe the importance of dimensionality reduction.
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.