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Quick info:

Instructor:
Vagelis Papalexakis
TA: Yorgos Tsitsikas
Where: Student Success Center | Room 308
When: TR
8:00am - 9:20am

Office Hours:

    Instructor:
    On Zoom (see Canvas)

    TA: On Zoom (see Canvas)

Quick Data Resources:

Class Description

This class is an introduction to fundamental concepts in Machine Learning and Data Mining, including clustering, regression, classification, association rules mining, and time series analysis. If time permits we will also introduce a few advanced concepts. The students will get hands-on experience through programming assignments, designed to get them familiar with data statistics and visualization, implementing, and applying classification techniques, as well as using, implementing, and applying clustering techniques.
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IMPORTANT INFORMATION REGARDING COVID-19

Please read how and what parts of the class will be adapted to be online as a result of the COVID-19 pandemic.

General Information

Instructor: Vagelis Papalexakis, Office @ MRB 3132
Office hours: Tuesday-Thursday 4-5pm and on demand | Zoom (check Canvas for up-to-date time and link).
e-mail: epapalexcs dot ucr dot edu (Subject should start with [CS171W22])
TA: Yorgos Tsitsikas e-mail: gtsit001 ucr dot edu (Subject should start with [CS171W22])
Office hours: On Zoom (check Canvas for up-to-date time and link).

Where: Student Success Center | Room 308 - According to the latest campus update, the first two weeks will be ONLINE (Zoom information on Canvas)
When: Tuesday - Thursday 8:00am - 9:20am

Textbook: Jiawei Han, Micheline Kamber and Jian Pei, Data Mining: Concepts and Techniques, 3rd ed., The Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791 Textbook website

Additional Optional Reading Material:
Charu C. Aggarwal, Data Mining: The Textbook, Springer, May 2015
Textbook website
Christopher Bishop Pattern Recognition and Machine Learning, Springer, ISBN 978-0-387-31073-2 Textbook website

Grading Scheme: The class will have three assignments, three quizzes, and a final exam. There will also be an extra credit assignment. The grading will be distributed as follows: Discussion: This term we will be using Piazza for class discussion. The system is highly catered to getting you help fast and efficiently from classmates, the TA, and myself. Rather than emailing questions to the teaching staff, I encourage you to post your questions on Piazza.
Find our class page at: piazza.com/ucr/winter2022/cs171ee142

Assignments

The assignment descriptions will be uploaded on Canvas.
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Submissions: All deliverables are due on Canvas by 11:59pm on their respective due date.

Late Policy: There is no delay penalty for medical or family-related emergencies. For each late day there will be a 10% reduction on the original grade.
Academic Integrity: Each assignment should be done individually. You may discuss general approaches with other students in the class, and ask questions to the instructor and TA, but you must only submit work that is yours. If you receive help by any external sources (other than the TA and the instructor), you must properly credit those sources, and if the help is significant, the appropriate grade reduction will be applied. If you fail to do so, the instructor and the TA are obligated to take the appropriate actions outlined here. Please read carefully the UCR academic integrity policies.
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Schedule (tentative)


You will find copies of the lecture slides on Canvas.

Below is the most up-to-date schedule for the class. Please keep checking below for updates
Make sure you scroll all the way to the right!
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