Machine Learning (AOT507)

This course introduces key Artificial Intelligence (AI) and Machine Learning (ML) concepts including supervised and unsupervised learning, core algorithms, performance evaluation, and model optimisation. The course also covers about neural networks, big data processes, and practical model deployment, preparing them to build and apply effective AI/ML solutions.

• Introduction to AI & ML
• Supervised & Unsupervised Learning
• Datasets
• Linear and Logistics Regression
• Decision Trees & Random Forests
• SVM
• Clustering, PCA
• Performance Metrics
• Hyper parameter tuning & optimisation
• Artificial Neural Networks
• Big Data Loop
• Deployment of Models

• Analyse the effectiveness of dimensionality reduction techniques such as prinicpal component analysis (PCA) and clustering in improving model performance.
• Evaluate various supervised and unsupervised learning algorithms for their suitability in solving complex data problems
• Critique model performance using relevant metrics and apply hyperparameter tuning to optimise results.
• Design machine learning models including regression, decision trees, support vector machine (SVM), and neural networks using appropriate datasets.
• Prepare scalable AI/ML solutions incorporating big data processing frameworks.
• Set up machine learning models in real-world environments, ensuring reliability and efficiency.