Machine Learning

Model Syllabus for Machine Learning

Credits: 5

Contacts per week: 3 lectures + 1 tutorial

MODULE I

Introduction to Machine Learning: Human learning and it’s types; Machine learning and it’s types; well-posed learning problem; applications of machine learning; issues in machine learning

Preparing to model: Basic data types; exploring numerical data; exploring categorical data; exploring relationship between variables; data issues and remediation; data pre-processing

Modelling and Evaluation: Selecting a model; training model – holdout, k-fold cross-validation, bootstrap sampling; model representation and interpretability – under-fitting, over-fitting, bias-variance tradeoff; model performance evaluation – classification, regression, clustering; ...