Hello, I am the module leader for the Data Mining module, that is part of the MSc Artificial Intelligence course. I will be delivering the module’s lectures and labs. This page shows the tentative schedule for the module.

Most of the content is based on the Book “Introduction to Data Mining” Second Edition by Tan, Steinbach, Karpatne and Kumar.

Tentative Schedule

Day 1	Lesson 1	Introduction to Data Mining
	Lesson 2	Introduction to data and its characteristics
	Lesson 3	Types of data
	Lesson 4	Data quality
	Lesson 5	Measures of similarity and dissimilarity
	Lesson 6	Data preparation and preprocessing
	Lesson 7	Information Theory basics
Day 2		Classification
	Lesson 8	Overview of conventional and deep learning classifiers.
	Lesson 9	Ensemble learning
	Lesson 10	Model overfitting
	Lesson 11	Model evaluation and imbalanced classes
Day 3		Feature Selection and Extraction
	Lesson 12	Feature selection
	Lesson 13	Filter approaches
	Lesson 14	Wrapper approaches
	Lesson 15	Embedded approaches
	Lesson 16	Hybrid approaches
	Lesson 17	Feature Extraction (SVD, PCA)
Day 4		Clustering Basics and Beyond
	Lesson 18	Characteristics of data and clusters
	Lesson 19	Clustering algorithms
	Lesson 20	Cluster validity measures
	Lesson 21	Soft clustering
	Lesson 22	Density-based clustering
	Lesson 23	Graph based clustering
		Developing Good Solutions
Day 5	Lesson 24	Outlier and anomaly detection
	Lesson 25	False discoveries
Days 6-10		Applications of Data Mining
Day 6	Lesson 26	Information Retrieval
Day 7	Lesson 27	Natural Language Processing
Day 8	Lesson 28	Biomedical Predictive Modelling
Day 9	Lesson 29	Image Retrieval
Day 10	Lesson 30	Coursework -project showcase videos from industry

Module Delivery

• Recommended text book: Introduction to Data Mining
• Slides: PPT slides will be available.
• Slides with recordings: Short versions of the slides with recordings for every lesson
• Labs: In MATLAB and/or Python.
• Weekly lab activities: In your preferred programming language
• Live discussion sessions