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Instructor
Farhad Mohsin [home]
Lecture times
TuTh 9:30AM - 10:45AM
Location
Swords 328
Office hours
Office hours location: Swords 339
- Wed: 12-1 PM
- Thu: 2-3:30 PM
Canvas
We'll use Canvas for assignment submission, lecture notes sharing etc.
All assignment's written reports must be submitted in pdf format. I would prefer a digital file (written in Word or LaTex), however it is fine if you put in pictures taken of handwritten assignments, as long as it's legible.
For assignments with coding components, they must be done in JuPyter Notebook, and then exported as pdfs. We will go through the procedure for this in class.
Course description
This course provides an introduction to Data Mining and will examine
data techniques for the discovery, interpretation and visualization of patterns
in large collections of data. Topics covered in this course include data mining
methods such as classification, rule-based learning, decision trees, association
rules, and data visualization. The work discussed originates in the fields of
artificial intelligence, machine learning, statistical data analysis, data
visualization, databases, and information retrieval.
Prerequisites
The prerequisite for this class is CSCI 132, Data Structures.
Also note that you'll have assignments that require programming in Python. The first couple of lecture will help brush up Python syntaxes and introduce (possibly) new Python libraries that are common in data mining.
Textbook
Data Mining and Machine Learning: Fundamental Concepts and Algorithms
Second Edition.
by Mohammed J. Zaki and Wagner Meira, Jr
Cambridge University Press, March 2020
ISBN: 978-1108473989
The textbook covers fundamental algorithms in data mining and machine learning. The book will specially be referenced for the theoretical concepts related to the course.
It is also not mandatory to buy the textbook as the book has a free online version, which you can access at https://dataminingbook.info/book_html/.
Exams
Midterm:
There will be one midterm exam held on the following date:
Midterm Exam: (Tentative) Oct 24
Final exam:
A cumulative final exam will be held during finals week as scheduled.
The class might have some in-class pop quizzes, mostly focusing on conceptual questions.
Homework Assignments
There will be several homework
assignments during the semester. These problem sets will include questions that require
written answers about concepts, and also problems that involve use of the Python libraries introduced in class.
Term Project
Students will work in groups of two to complete a term project. Details regarding the project will be discussed on the first day of class.
Grading
- Homework: 25%
- Term Project: 25%
- Midterm exam:20%
- Final exam: 30%
Late Policy
Assignments are due before the beginning of class on the assigned due date.
Late assignments will be marked down 10% for each day late. That is, assignments
turned in after the time they are due will be marked down 10%, assignments turned
between 24 and 48 hours after the due date will be marked down 20%, and so on. The
penalty will be determined when the assignment is physically transferred to the
instructor or submitted online (whichever is the submission method for that particular assignment).
Late work will not be accepted after the graded assignment is returned to the class.
Collaboration Policy
You are allowed to discuss strategies for solving homework problems
with other students, however any work you turn in must be your own work (i.e.
you may not simply copy another student's answers and turn them in as your own).
For the group project, you will work together, but the contribution of each student should clearly be written in the final project report.
You must clearly indicate the names of any students you work with on
each assignment.
You may consult public literature (books, articles, etc) for information,
but you must cite each source of ideas you adopt.
Please familiarize yourself with the
Math and CS Department's policy on Academic Integrity
as well as the
College's Academic Integrity Policy.
Excused Absence Policy
Class attendance is expected and will be counted toward the participation part
of the grade. If you have a confirmed reason why you cannot attend an exam at
the day or time it is given, you must contact your instructor well ahead of
time to arrange to take it at another time. Please see the
College Policy on excused absences.
Reasonable Accommodations and Accessibility Services
The instructor is committed to providing students with disabilities equal access
to the educational opportunities associated with this course. For details or to
request accommodation, please refer to
College procedures on Requests for Reasonable Accommodations
and the
Office of Accessibility Services.
Class Recordings
Consistent with applicable federal and state law, this course may be
video/audio recorded as an accommodation only with permission from the Office of
Accessibility Services.
Last modified: Sep 4, 2023
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