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Instructor
Farhad Mohsin [home]
Lecture times
TuTh 11:00AM - 12:15PM
Location
Swords 227
Open hours
Office location: Swords 339
- Mondays 3:30-5:00pm
- Tuesdays 1:00-2:00pm
- Thursdays 10:00-11:00am
- and by appointment and Zoom
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
There is no required textbook for this course. The field of data mining
is ever-changing and I plan to teach from many different sources over the semester.
However, I do recommend the following textbooks if you want to do self-study
on fundamental data mining concepts from a somewhat theoretical/algorithmic perspective.
- Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Machine Learning: Fundamental Concepts and Algorithms, 2nd Edition, Cambridge University Press. ISBN: 978-1108473989.
- Jure Leskovec, Anand Rajaraman, Jeff Ullman, Mining of Massive Datasets, 3rd Edition or 2nd edition. 3rd edition only available online at http://www.mmds.org/. The 2nd edition is available from Cambridge University Press. ISBN: 9781316147313
Both books are freely available online which you can access at https://dataminingbook.info/book_html/ and http://www.mmds.org/.
Exams
Midterms:
There will be two or three mid-term quizzes/exams.
Final exam:
A cumulative final exam will be held during finals week as scheduled.
Homework Assignments
There will be up to ten 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.
Follow-up Discussion
To ensure academic integrity, I will randomly select students after each
assignment to discuss their submitted work. You will participate in two or three
such conversations during the semester. These informal chats give you an
opportunity to walk me through your solutions and thinking process,
further demonstrating your understanding.
Grading
- Homework: 45%
- Midterm exam:30%
- Final exam: 25%
Late Policy
Assignments will usually be due at midnight of the submission date.
You will have a total of 7 late days throughout the semester,
which you can use without penalty.
However, you can use a maximum of 3 days late for a single assignment.
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).
You must clearly indicate the names of any students you work with on
each assignment.
Clarification about Artificial Intelligence or "generative AI"
Generative AI models like ChatGPT, Claude, Gemini, GitHub/Microsoft Copilot,
or similar code generation tools are clearly useful. For the purposes
of CSCI 307 and the collaboration policy, you should treat generative AI
models as if it were a person -- say, a very well-read, sometimes clever,
and very confident but sometimes unreliable roommate. That means it is okay
to ask a model for general help understanding class material, but it is
not okay to put homework questions into a model, or to ask the model to
solve specific tasks that an assignment has tasked you to perform. That
crosses the line into simply cheating, just as asking a roommate to do
your homework would be a violation of the College academic integrity policy.
If you consult or use generative AI in any of your assigned work,
you must cite the specific tools you used and provide a list of all prompts
you used in your discussion log. You are still responsible for ensuring the
correctness and accuracy of all submitted work. In addition, you are
responsible for ensuring that all source materials used in your work are
properly cited and be aware that generative AI can often produce output
copied closely (or sometimes directly) from source material without
properly citing those sources. Failure to correctly and fully cite
sources constitutes plagiarism and is a violoation of the academic integrity policy.
You may consult publicly available literature (books,
articles, blog posts, coding tutorials 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: Aug 22, 2025
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