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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

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

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.


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.

  • Exams (60%): There will be three exams covering the main topics of the course. The tentative exam dates are noted on the class schedule table. There is no comprehensive final exam. All exams are open book.
  • Assignments (40%): The assignments are meant to be practically oriented, thought they may include other questions. 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 (5-6 assignments in total). There may be a final project assignment on some real-world data analysis challenge if a suitable public challenge problem is made available in the latter half of the semester.
Other Policies
  • Attendance: Students are strongly encouraged to participate in the class, and should try to attend all classes. Students are responsible for any topics and assignments for the missed classes.
  • Late Assignments: Most assignments will be due just before midnight on the due date. Students can get an automatic one day extension for a 15% grade 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.

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