Spring 2021
[Announcements] [ Syllabus ] [Schedule] [ Lecture notes ] [ Project/Exercise Lab/Assignment ]
Class: Tuesday 6:00 PM ~ 8:30 PM (Classroom: Mandeville Hall, Room 201)
Instructor: Dr. Jeongkyu Lee
E-Mail: jelee@bridgeport.edu
Website: http://www1bpt.bridgeport.edu/~jelee/courses/CS552_S21/CS552_S21.htm
Phone: (203) 576-4397
Online Office : https://bridgeport.zoom.us/j/5346018351 (Personal Zoom Link)
Office Hours: Online office hour only using Zoom. To make an appointment with Prof. Lee: https://appoint.ly/s/jelee0408/office
GA:
Vamsi Varma Datla
E-Mail: vdatla@my.bridgeport.edu
Office Location: Online using Zoom
Office Hours: Make a reservation for meeting at https://appoint.ly/s/vamsivarma/officehours| 1/1/2021 | CPSC552 class website open. |
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Text Book
Data Mining: Concepts and Techniques, 3rd Edition, by Jiawei Han, Micheline Kamber and Jian Pei, published by Morgan Kaufmann Publishers, 2011. ISBN 0123814790.
Book web page: http://www.cs.uiuc.edu/~hanj/bk3/
Deep Learning by Ian GoodFellow, Yoshua Benjio and Aaron Courville, 2016, MIT Press
Course Objective and Outcome:
Data mining algorithms and techniques including data preprocessing, mining frequent pattern, association rules, classification and predication, decision tree, bayesian classification, cluster analysis, partitioning and hierarchical clustering, density-based clustering and grid-based clustering algorithms.
Grading Policy:
Lab (programming) (2) 25%
Presentation 10%
Reading Assignments (3) 10%
Mid-term Exam 25%
Final Exam 25%
Attendance 5%
Grade Distribution:
A = 100 to 90, B = 89 - 75, C = 74 - 60, D = 59 - 50, F = 49 and Below
Attendance and Drop Policy
Attendance required and will be scored.
General Policies:
Students are responsible for checking this web site frequently for course related material and announcements. This site will be the primary form of communication for the course unless otherwise specified.
Please include "CPSC552" in the Subject line of all e-mail correspondence.
Any homework or projects assigned is due at the end of the class on the due date. There is a 10% penalty for every 24 hours being late, or fraction thereof, beyond the deadline. Maximum latency is 5 days beyond which a grade of zero will be assigned.
No make up exams or assignments will be given.
All exams are closed book.
The instructor reserves the right to modify the policies, calendar, assignments, point values and due dates.
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Note: Some sections of certain chapters may be omitted in case we run out of time. The lists of excluded sections will be specified during lectures, if any. Lecture material and due dates may be adjusted as the course progresses.
|
Week |
Date |
Covered Topics |
Comments |
|
1 |
1/27 |
Chapter 1 (2nd: Chapter 1): Introduction Chapter 2 (new): Getting to Know Your Data |
|
| 2 |
2/3 |
Review of Math in
Data Mining |
|
|
3 |
2/10 |
Chapter 3 (2nd: Chapter 2): Data
Preprocessing Exercise Lab 0 |
RA 0
due on 2/12 |
| 4 |
2/17 |
Chapter 6 (2nd: Chapter 5): Mining Frequent Patterns, Association Rules Exercise
Lab 1 |
Project Phase 1: Due
on 2/19 |
|
5 |
2/24 |
Chapter 8 & 9 (2nd: Chapter 6): Classification and Prediction, Decision Tree Exercise
Lab 2 |
Lab
1 due on 2/26 |
| 6 |
3/3 |
Chapter 8 & 9 (2nd: Chapter 6): Bayesian Classification Exercise
Lab 3 |
Project Phase 2: Due
on 3/5 |
|
7 |
3/10 |
Deep Learning 1: Introduction to Deep Neural Networks, Convolutional Neural Networks Exercise Lab 4 |
RA
2: due on 3/12 |
|
8 |
3/17 |
Deep Learning 2: Recurrent Neural
Networks, Deep Generative Models Exercise Lab 5 |
RA 3 due on 3/19 |
| 9 |
3/24 |
Machine
Learning with Google Cloud Exercise Lab 6 |
|
|
10 |
3/31 |
Mid-term Exam: March 31 (Wed) |
Mid-term
Exam: Ch 1, 2, 3, 6, 8, 9 and Deep Learning on 4/1 Project
Phase 3: Due on 4/2 |
| 11 |
4/7 |
Chapter 10 & 11 (2nd Chapter 7): Cluster Analysis-Introduction, Partitioning & Hierarchical Clustering | Lab 2 due on 4/9 |
|
12 |
4/14 |
Chapter 10 & 11 (2nd Chapter 7): Density-based Clustering, Grid-based & Model-based Clustering |
RA
4: due on 4/16 Project
Phase 4- Poster: Due on 4/16 |
| 13 |
4/21 |
Project- Presentation | Project Phase
4-Writing-up: Due on 4/23 |
|
14 |
4/28 |
Project-
Presentation |
Project Phase 4: Oral Presentation |
|
15 |
5/3
~ 5/7 |
Project
- Workshop |
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Chapter 1 (2nd: Chapter 1): Introduction
Chapter 2 (new): Getting to
Know Your Data
Chapter 3 (2nd: Chapter 2): Data Preprocessing
Chapter 4: Data Warehouse and OLAP Technology
Chapter 5: Data Cube Technology
Chapter 6 (2nd: Chapter 5): Mining Frequent Patterns, Associations, and
Correlations
Chapter 7 (2nd: Chapter 5):
Advanced Pattern Mining
Chapter 8 (2nd Chapter 6): Classification: Basic Concept
Chapter 9 (2nd Chapter 6):
Classification: Advanced Methods
Chapter 10 (2nd: Chapter 7): Cluster Analysis: Basic Concept and
Methods
Chapter 11 (2nd: Chapter 7): Advanced Cluster Analysis
* All lecture notes are
available at Canvas.
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LAB/Reading Assignments/Presentation
Reading Assignments
Read the given materials, and submit 2 or 3 pages of summary report by due date. No late submission accepted.
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