syllabus

teach

About the Me!!!

Data mining is the study of efficiently finding structures and patterns in large data sets. We will focus on several aspects of this:

(1) converting from a messy and noisy raw data set to a structured and abstract one,
(2) applying scalable and probabilistic algorithms to these well-structured abstract data sets
(3) formally modeling and understanding the error and other consequences of parts (1) and (2), including choice of data representation and trade-offs between accuracy and scalability.

Supervised Learning - labelled data and task driven
1. regression — to predict one or more real values
2. classification — to predict one of a finite number of possible outcomes
3. probabilistic supervised learning — to predict a distribution of outcomes
Unsupervised Learning — unlabelled data and data driven to develop a data model
1. clustering - divide by similarity
2. association - identify sequence
3. dimensionality reduction - wider dependencies
Optimization — to fit or choose parameters in all of the models above

All the course announcement and additional materials or tutorials to help you learn the cource will be posted in the blog page of the course website.

Every assignment in this course is due at exactly the time stated and while I will grade late assignments, there will be a marks deduction.

The course evaluation will be a weighted mask score on class attenance and participation, homework, quizes, projects and exams.