Instructor  Feng Chen 
Office  LI96J 
Number  (518) 4424270 
fchen5@albany.edu  
Office Hour  Monday: 4:15PM to 5:15PM
Wednesday: 11:00AM to 12:00PM 
TA  Lin Zhang

Office  CS lounge 
Number  
lzhang22@albany.edu  
Office Hour  Monday: 5:45PM to 7:45PM
Wednesday: 11:40AM to 2:40PM 
TA  Jonathan Song

Office  CS lounge 
Number  
njsong@albany.edu  
Office Hour  Monday: 12:30PM to 2:30PM
Thursday: 1:00PM to 3:00PM 
TA  Huang, Trey


Office  CS lounge  
Number  
thuang2@albany.edu  
Office Hour  Friday: 4PM to 6PM
Saturday: 4AM to 6PM 
TA  Makkar, Nippun

Office  CS lounge 
Number  
nmakkar@albany.edu  
Office Hour  Monday 5:45PM T0 7:45PM
Wednsday 1:00PM T0 3:00PM 
TA  Nian, Xiaohu

Office  CS lounge 
Number  
xnian@albany.edu  
Office Hour  Monday 12:30PM T0 2:30PM
Wednsday 12:30PM T0 2:30PM 
Class Time and Location  MoWe 2:45PM  4:05PM, LC 05 
Class Website  http://www.cs.albany.edu/~fchen/course/2015ICSI431531/ 
Course Postings
Course Description:
A course on data mining (finding patterns in data) algorithms and their application to interesting data types and situations. We cover algorithms that addresses the five core data mining tasks: prediction, classification, regression, clustering, and associations. Course projects will involve advanced topics such as algorithm developments for handling large data sets, sequential, spatial, and streaming data. Prerequisite(s): A Csi 310.
TextBook
Introduction to Data Mining PangNing Tan, Michael Steinbach, Vipin Kumar AddisonWesley, 2005 ISBN10: 0321321367 ISBN13: 9780321321367 

Data Mining: Concepts and Techniques (2nd Edition) Jiawei Han, Micheline Kamber Publisher: Morgan Kaufmann, 2011 ISBN10: 0123814790 ISBN13: 9780123814791 
Course Description:
The schedule indicates the concepts and material to be covered in each week under the column labeled "Topics". Each topci with "*" mark will be presented by a six member team.
Course Project Requirement
Course Project teams:
Team Member  Team Project Title

Presentation Schedule

1 Nicholas Brown
Darshana Rane
Vinny Cerchia 
5/11, order 1 (8 minutes)  
2 Yizhen Chen
Wentao Liu
Hanyu xue 
5/6, order 3 (8 minutes)  
3. Phuc Bui
Hang Lin
Xuanyi Lin 
The prediction of transportation  5/11, order 13 (8 minutes) 
4 Samarth Shah
Gaurav Ghosh
Summit Hotwan 
SomePlaceElse  5/11, order 1 (8 minutes) 
5 Anthony Paradiso
Aashish Chaudhary
Eric Zeissler 
5/11, order 4 (8 minutes)  
6 Sam Pellino
Priya Balachandran
Saurabh Saxena 
5/11, order 2 (8 minutes)  
7 Vaibhav Kapse
Subhash Chandra Kilari
Rahul Srivastava 
Fraud Detection  5/11, order 14 (8 minutes) 
8 Paul Tomch
David Vadney
Daniel Hono 
5/11, order 4 (8 minutes)  
9 Lili Guo
Rui Wang
Yang Vincent 
5/11, order 16 (8 minutes)  
10 Ryan Dubowsky
Margaret Dubowsky 
5/11, order 3 (8 minutes)  
11 apurva kulkarni
prafull soni
akhil chaturvedi 
5/11, order 2 (8 minutes)  
12 Aaron champagne
Oguz aranay
Rafael Veras
Jonathan Shepard 
5/11, order 7 (8 minutes)  
13 Akash Shashikant Gawade
Shivam Agrawal
Harshad Bhanushali 
Potential Car Buyers  5/11, order 12 (8 minutes) 
14 Baojian Zhou
Zeyang Wu
Russell Sean 
5/11, order 11 (8 minutes)  
15 Congzhou Wang
Steven Heiple
Sushant Obeja 
'College Culture': A Twitterbased System for Recommending Colleges to High School Students  5/11, order 10 (8 minutes) 
16 Justine Buddie
Mike Scalera
Greg R Scalera 
5/11, order 15 (8 minutes)  
17 Dhruv Patel
Lars Hansen
Yuhan Zhang 
Partify  5/11, order 5 (8 minutes) 
18 Kushagra Sharma
Dhiraj Tanwar
Bilal Khan 
5/11, order 5 (8 minutes)  
19 Josh Gibbons
David Noftsier
Lin Yun 
5/11, order 9 (8 minutes)  
20 Kanakamedala Rajesh
Chenna Rohith Raj 
Estimation of Crime within a city based on previous Crime rate  5/11, order 6 (8 minutes) 
21 Botla Sai Prasanna Kumar
Bangaru Bhavana
Mangu Vamsee Jagannath 
recommendation systems for movies  5/11, order 8 (8 minutes) 
References for Lecture Topics:
1. Decision Tree
[1] Decision Tree Lecture Slides: http://wwwusers.cs.umn.edu/~kumar/dmbook/dmslides/chap4_basic_classification.ppt (http://wwwusers.cs.umn.edu/~kumar/dmbook/dmslides/chap4_basic_classification.pdf)
[2] Decision Tree 7 minutes tutorial video: https://www.youtube.com/watch?v=a5yWr1hr6QY
2. Logistic Regression
[1] Machine Learning with Python  Logistic Regression: http://aimotion.blogspot.com/2011/11/machinelearningwithpythonlogistic.html
[2] A Tutorial in Logistic Regression: http://www.statpt.com/logistic/demaris_1995.pdf
Examinations and Assignments:
There are around 12 homework assignments. Homework assignments are due at the start of class. If you have an excused absence from a class, turn in the homework assignment prior to the class session. All assignments must have your name, student ID and course name/ number.
Late Submission Policy:
Assignments must be submitted before the class on the specified due date (Monday of designated week). A penalty of 30% will be deducted from your score for the first 24hour period if your assignment is late. A penalty of 70% will be deducted from your score for >= 24hour period. Assignments submitted more than 3 days late will not be assessed and will score as a zero (0). Weekend days will be counted. For assignments, you are encouraged to type your answers.
Policy on Cheating:
Cheating in an exam will result in an E grade for the course. Further, the students involved will be referred to the Dean's oce for disciplinary action.
Homework problems are meant to be individual exercises; you must do these by yourself. Any of the following actions will be considered as cheating.
Cheating in a homework exercise will result in the following penalty for all the students involved.
Students who cheat in two or more homeworks will receive an E grade for the course. The names of such students will also be forwarded to the Dean's oce for disciplinary action.
Attendance:
Class attendance is required and checked. Each case of missing class without a proper explanation will cause 20% less from your final numerical grade. If you miss a class, it is your responsibility to find out the material covered in the class. There will absolutely no makeup classes. Only in specific, unavoidable situations students are allowed to excuse absences from class: 1) personal emergencies, including, but not limited to, illness of the student or of a dependent of the student, or death in the family [Require doctor's note]; 2) religious observances that prevent the student from attending class; 3) participation in Universitysponsored activities, approved by the appropriate University authority, such as intercollegiate athletic competitions, activities approved by academic units, including artistic performances, academic field trips, and special events connected with coursework; 4) governmentrequired activities, such as military assignments, jury duty, or court appearances; and 5) any other absence that the professor approves.
Grading:
Homework Assignments : 35%  Exam: 30%  Presentation: 5%  Final Project (3member team): 25%  Class Discussion and Participation: 5%