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This module aims to develop the fundamental concepts of Machine Learning. At the end of this module, the students will be able to apply basic machine learning algorithms to data-driven problems. The analytic content of this module is:
• Basic concepts: the students will learn what is the main aim of ML, overfitting and underfitting, dataset splits, bias-variance dilemma.
• Mathematics: vectors, matrices, norms, determinant, matrix inverse, eigenvalues and eigenvectors, convex optimisation, derivative.
• Classifier: binary classification, linear separability, kernel trick, SVM, decision trees, random forest.
• Regression: linear regression, logistic regression, ridge regression, SVR.
• Dimensionality Reduction: PCA, ICA, Autoencoders.

Tools used in this module: Python.

LO1: Understand the basic concepts of machine learning
LO2: Understand basic concepts of linear algebra and calculus
LO3: Understand basic machine learning algorithms and their applicability
LO4: Apply machine learning algorithms to dataset and perform critical evaluation
LO5: Design, develop, and evaluate data driven applications in Python
LO6: Present the results in a scientific form and critically draw appropriate conclusions.