• Nov 70: Assignment 6 has been posted.
• Nov 7: Assignment 5 has been posted.
• Oct 25: updated chap8.pdf on PCA, kernel PCA and SVD.
• Oct 24: Assignment 4 has been posted.
• Oct 14: Assignment 3 has been posted.
• Sep 25: Assignment 2 has been posted.
• Sep 17: Assignment 1 has been posted.
• Sep 14: Activate your piazza account
• Sep 12: Book chapters, as well as lectures are posted online after each lecture. Make sure to check the course website.
• Aug 18: Course website is up, with the tentative calendar and syllabus.

Introduction

Data mining is the process of automatic discovery of patterns, models, changes, associations and anomalies in massive databases. This course will provide an introduction to the main topics in data mining and knowledge discovery, including: algebraic and statistical foundations, pattern mining, classification, and clustering. Emphasis will be laid on the algorithmic approach.

Learning Objectives

After taking this course students will be

• able to describe the fundamental data mining tasks like pattern mining, classification and clustering
• able to analyze the key algorithms for the main tasks
• able to implement and apply the techniques to real world datasets

Prerequisites

The pre-requisites for this course include data structures and algorithms and discrete mathematics. Linear algebra and probability & statistics are also essentially pre-requisites, though an attempt will be made to review the basic concepts. Assignments will require the use of the python language, with NumPy package for numeric computations. You are expected to learn python on your own via web tutorials, etc. Assignments must be submitted via email to .

Textbook

There is no required text for the course. Notes will be posted online on the course webpage.

The following text books are also good references:

• Introduction to Data Mining, by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Addison Wesley, 2006.
• Data Mining: Concepts and Techniques (2nd edition), by Jiawei Han and Micheline Kamber, Morgan Kaufmann, 2006.

Grading Policy

Your grade will be a combination of the following items.

• Assignments (40%): The assignments are meant to be practically oriented. You'll be asked to implement some algorithms and apply them to real datasets, to complement the theory. There will be roughly one assignment every two weeks.
• Exams (60%): There will be three exams covering the main topics of the course. The tentative exam schedule is posted on the class schedule table. There is no comprehensive final exam. All exams are open book.

Other Policies

• Attendance: Students are strongly encouraged to participate in the class, and should try to attend all classes. Students are responsible for brushing up on any missed material.
• Laptops: Absolutely no laptops will be allowed in class during lectures. The only exception is during exams, to access the class notes online and to use the calculator. Even during the exam, you may not use any other software (e.g., R, python, matlab, etc.) for the computations, and you may not "browse" for solutions (you are not likely to find anything!).
• Late Assignments: Most assignments will be due just before midnight on the due date. Students get an automatic one day extension with 20% penalty. No late assignments will be accepted after the midnight following the due date.

Academic Integrity

You may consult other members of the class on the assignments, but you must submit your own work. For instance you may discuss general approaches to solving a problem, but you must implement the solution on your own (similarity detection software may be used). Anytime you borrow material from the web or elsewhere, you must acknowledge the source.

The school takes cases of academic dishonesty very seriously, resulting in an automatic "F" grade for the course. Students should familiarize themselves with the relevant portion of the Rensselaer Handbook of Student Rights and Responsibilities on this topic.