From Mohammed J. Zaki

Dmcourse: Syllabus

Introduction

This course will provide an introduction to the main topics in data mining and knowledge discovery, including: algebraic and statistical foundations, pattern mining, classification, regression, and clustering. Emphasis will be laid on the algorithmic approach.

Learning Objectives

After taking this course students will be

Prerequisites

The pre-requisites for this course include data structures and algorithms and discrete mathematics. Linear algebra and probability & statistics are also 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.

Textbook

The main required textbook for the course is:

Readings from the book will be posted on the course schedule, and supplementary material will be provided when necessary.

Grading Policy

Your grade will be a combination of the following items.

Other Policies
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.

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Page last modified on August 25, 2017, at 11:03 PM