Course

Syllabus

Introduction to machine learning and machine learning applications. Data featurization, vectorization, linear algebra, and matrix representations. Supervised learning – linear regression, polynomial regression, logistic regression, Decision Trees, Support Vector Machine and ANN. Regularization, tuning, overfitting, underfitting. Unsupervised learning: Clustering, dimensionality reduction (PCA). Deep Neural networks: multilayer perceptron, transfer learning, edge models. ML model evaluation metrics. Generative AI – LLMs. MLOps – introduction to converting ML models from test bench to production (saving, loading, using trained models). 

Learning Outcomes 

LO1 To introduce different machine learning paradigms. 

LO2 To provide understanding of machine learning algorithms to be used on a given dataset for regression/classification problems. 

Course Outcomes 

CO1 Ability to conduct data analysis and data visualization. 

CO2 Apply the complete ML pipeline in real-world dataset – Analyse datasets, decide pre- processing steps, visualize data, apply ML models, and infer the meaning based on different performance metrics. 

1. An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani (2022)
2. Géron, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O’ Reilly Media, 2019.