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  Yizhen Chen

Office  CS lounge 
Number  
jonkiky@gmail.com  
Office Hour  Tuesday 10:00AM T0 12:00PM
Thursday 10:00AM T0 12:00PM 
TA  Rui Wang

Office  CS lounge 
Number  
rwang3@albany.edu  
Office Hour  Thursday 4:45PM to 6:45pm
Saturday 12:00PM to 2:00PM 
TA  Chunpai Wang

Office  CS 095L 
Number  
cwang25@albany.edu  
Office Hour  Tuesday 11:45AM to 13:45PM
Friday 2:00PM to 4:00PM 
TA  Yuhan Zhang

Office  CS lounge 
Number  
yzhang38@albany.edu  
Office Hour  Monday 12:40PM to 2:40PM
Wednesday 12:40PM to 2:40PM 
Class Time and Location  MoWe 2:45PM  4:05PM, LC 19 
Class Website  http://www.cs.albany.edu/~fchen/course/2016ICSI431531/ 
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
Data Mining, The Textbook Charu C. Aggarwal Springer, 2005 ISBN: 9783319141411 
Introduction to Data Mining PangNing Tan, Michael Steinbach, Vipin Kumar AddisonWesley, 2005 ISBN10: 0321321367 ISBN13: 9780321321367 
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:
to be announced
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%
Course Project Groups:
Group 1 5 TA: Yizhen ChenGroup Name  Members  Project Title  Presentation Date 
1  Erkang Xie (UG), Ziyun Zeng (UG) 

2  Mounika Ryakala (Grad), Yutthana Srisakunkhunakorn (Grad), Manish Chandra (Grad) 

3  Navita Jain (Grad)  
4  Amit Pal Singh (Grad), Navodit Ranjan (Grad), Maxx Sawyer (UG) 

5  Komal Narwekar (Grad), Shilpa Ramesh (Grad) 

6  Pooja Patel (Grad), Dhaval Lad (Grad), Andrew Desbiens (UG) 

7  Arun Sharma (Grad), Vimalkumar Chellam (Grad) 

8  Kevin Smith (Grad), James Martine (UG), Zane Coonrad (UG) 

9  Ashish Yeshwant Jadhav (Grad), Ravikiran Pathade (Grad), Marcus Seixas (UG) 

10  Abhishek Gupta (Grad), Manas Gaur (Grad), Rafael Leitao Oliveira (UG) 

11  Shanmugar Rathinasamy Mariappan (Grad), Varun Chandrasekar (Grad), Michael Seredensky (UG) 

12  Siqi Wang (UG), Tianqi Zhao (UG), Xiaojun Feng (UG) 

13  Sayali Thorat (Grad), Steven Cifareli (UG), Brian Ethier (UG) 

14  Ashish Agarwala (Grad), Meley Kifleyesus (Grad), Zachary Carciu (UG) 

15  Neel Patel (Grad), Nisarg Shah (Grad), Julia Turner(UG) 

16  Abhiram Mocharla (Grad), Shivam Awasthi (Grad), Priyanka Dagar (UG) 

17  Aatman Togadia (Grad), Smit Shilu (Grad), Akanksha Atrey (UG) 

18  Estesham Ahmed Quadri Syed (Grad), Lokesh Rishi (Grad), James Pica (UG) 

19  Chen Zhao (Grad), Andrew Janucik (Grad), Anthony Cochetti (UG) 

20  Jamie Lee (Grad), Randolf DeSouto (UG), William Thomas (UG) 

21  Nathaniel Gottschalt (UG), Reena Sharma (Grad), Garikapati Geethika (Grad) 

22  Ananya Subburathinam (Grad), Namrata Galatage (Grad), Chandana Ravella (Grad) 