Times: Wednesday 4:30pm – 7:00pm
Location: Sherman Hall 210
Instructor: Nirmalya Roy
Instructor’s Office Location and Hours: ITE 421 Monday 1:30 – 3:00pm, or by appointment
Instructor’s Email: nroy at umbc dot edu
Course Descriptions: Computational methods are inevitable tools for many facets of information systems research. These methodologies are used as fundamental tools and techniques in research and advanced practice in information systems, with particular focus on networking hardware and software technologies that deal with data and systems. Data becomes useful when it provides meaningful information through data analysis and mining, pattern recognition and learning, information extraction and visualization. System becomes useful when it meets the required end performance metrics through the governing policies and procedures and underlying models and simulations. Sophisticated data analysis and system performance measurements require a mixture of skills ranging from algorithmic foundation, data mining, machine learning, computational modeling, and information systems performance evaluation. This course covers the mixture of these skills with the goal of providing information science graduate and masters students with the ability to employ them in future research. The course is projectbased, allowing students to understand the use of computational methods to pursue research objectives and interests.
Course Objectives: The purpose of this course is to provide a comprehensive foundation to apply computational research methods in solving problems in Information Systems. This course should enhance students’ reasoning, problemsolving and modeling abilities, particularly in dealing with algorithmic problems. More specifically, the course has the following objectives:
• Familiarize students with the concepts and applications of computational techniques (machine learning, data science, graph theory, computational complexity, information and communication technology, operational managements etc.) to solve computational problems.
• Teach students how to think and formalize problems algorithmically and experimentally.
We will not assume any background beyond high school level mathematics and familiarity with programming concepts. However, students are expected to spend time in learning the concepts in this course, many of which will be covered in details.
Course Topics:
• Algorithmic Complexity
• Information and Communication Technology
• System Modeling and Performance Measurements
• Data Science
• Machine Learning
• Applications
Course Overview:
• Each student will be required to present 1 paper in the entire semester.
• There will be one midterm exam, and no final exam.
• There will be a development project (programming/testbed development). The main idea is to use real devices to build real systems which help collect data and albeit information latter! Example is developing smart home activity recognition data collection toolkit, or developing a household appliance energy metering system, or sensor based stress monitoring system or a system which keeps an eye on your diet. We will be using offtheshelf commercially available devices, smart phones, wireless sensors, smart plugs or any other devices as need be for this purpose. The main intuition behind this semester long development project is getting real data for your research project. Energy education related project equipment will be sponsored by Constellation Energy while all other equipment will be provided by the instructor.
• The next most important component of the course will be the final research project obviously based on the computational methods you learn in this class. You are expected to do these projects in groups of 2 (preferably). Think about the research project problem early, discuss your ideas with me, read papers in the area, formulate your solution, and finally get the data from your development project and implement, and present it to the class.
Course Prerequisites: IS 650 (Data Communication and Networks) or IS 733 (Data Mining) or consent of the instructor
Recommended Textbooks (Optional):
 Introduction to Machine Learning, Second Edition, by Ethem Alpaydin, MIT Press, 2010 (Amazon.com)
 Fundamentals of Queueing Theory, 4^{th} Ed., by Donald Gross & John F. Shortle & James M. Thompson & Carl M. Harris. John Wiley & Sons, Inc, 2008 (Amazon.com)
 Data Structures and Algorithm Analysis in C++ (4th Edition) by Mark Allen Weiss, AddisonWesley, 2013 (Amazon.com)
Course Requirements and Grading:
Course Participation & Class Presentation  10% 
Homeworks, Quizzes & Programming Assignments  30% 
1 Midterm Exam  30% 
Research & Development Project  30% 
Tentative Course Schedule:
(Subject to change as the semester progresses)
Week  Date  Topic  Handout/Assignment  Due  Notes 
1  1/27  Course overview, logistics, etc.  Course Syllabus  Class Cancelled due to the Snow  
2  2/3  Introduction to Algorithm Analysis and System Modeling  Introduction  
3  2/10  Introduction to Machine Learning
Math Review for Computational Methods and Algorithm Analysis 
Homework 1 Presentation Logistics 
Introduction ML (Textbook IML: Chapter 1, Sections 1.1 and 1.2)
Math Review 1 (Textbook DSAA: Chapter 1, Section 1.2) 

4  2/17  Computational Complexity  Homework 2
Quiz 1 
Homework 1 (Solution)
Research 
Comp. Complexity (Textbook DSAA: Chapter 2, Section 2.1, 2.2, 2.3, 2.4) 
5  2/24  Sorting Algorithm Analysis [Insertion sort, Selection sort, Shellsort, Mergesort, Quicksort, Decision trees, Counting Sort, External sorting (Multiway Merge) etc.]  Sorting Algo. Analysis (Textbook DSAA: Chapter 7, Section 7.1, 7.2, 7.4, 7.6, 7.7, 7.9, 7.10, 7.11)  
6  3/2  Research Paper Presentation  Homework 2 (Solution)  
7  3/9  Research Paper Presentation  
8  3/16  Spring Break  
9  3/23  Introduction to Graph Algorithms, Topological Sort
Testbed development project progress pitch 
Homework 3  Testbed development project 3minutes madness slide due by 3/22 
Intro Graph Algorithms (Textbook DSAA: Chapter 9, Section 9.1, 9.2) 
10  3/30  Shortest Paths; Network Flow; Minimum Spanning Tree Applications (Prim’s and Kruskal’s Algorithms)  Shortest Path Network Flow MST (Textbook DSAA: Chapter 9, Section 9.3, 9.4, 9.5) 

11  4/6  Machine Learning: Supervised Learning; Bayesian Learning  Homework 4  Homework 3 Solution (extended to 4/13)  Supervised (Textbook IML: Chapter 2, Sections 2.1, 2.3, 2.4, 2.5, 2.7, and 2.8) Bayesian (Textbook IML: Chapter 3, Sections 3.1, 3.2 and 3.6) 
12  4/13  Introduction to TND, Statistics of Things Waiting in the Line (Queueing Theory), Characteristics of Queueing Process
Erlang Concept, Basic Model & Notation, Little’s Theorem Poisson process & Exponential distribution, Markovian Property, Memorylessness, Stochastic Process, Markov Process 
Homework 3  Intro Queueing Theory (Textbook FQT: Chapter 1, Sections 1.1, 1.2, 1.3, 1.4, 1.5, 1.7, 1.8, and 1.9)  
13  4/20  Birth & Death process, Markovian Systems
Single Server system: M/M/1Queue; steady state probabilities, M/M/1 performance measures Exam Review 
Homework 5  Homework 4  Simple Queueing Model (Textbook FQT: Chapter 2, Sections 2.1 and 2.2) Exam Review 
14  4/27  Exam  
15  5/4  Final Research & Development Project Presentation  Homework 5  Final Project Report Template 
Research Paper Presentation Schedule:
Peter Guerra: RunBuddy: a smartphone system for running rhythm monitoring; ACM Ubicomp 2015
Foteini Cheirdari: MyBehavior: automatic personalized health feedback from user behaviors and preferences using smartphones; ACM Ubicomp 2015
David Welsh: Monitoring building door events using barometer sensor in smartphones, ACM Ubicomp 2015
Arpita Roy: Unobtrusive Sensing Incremental Social Contexts using Fuzzy Class Incremental Learning, ICDM 2015
Timothy Michael Casey: Mindless computing: designing technologies to subtly influence behavior, ACM Ubicomp 2015
Vishak Iyer: Empath2: A Flexible Web and Cloudbased Home Health Care Monitoring System, ACM PETRA 2015
Navneet Kaur: Your reactions suggest you liked the movie: automatic content rating via reaction sensing, ACM UbiComp 2013
Mohammad Alodadi: Connecting Personalscale Sensing and Networked Community Behavior to Infer Human Activities, ACM Ubicomp 2014
Supplementary Materials: Links to Research Papers and Possible Research Projects:
Good conferences and workshops in broad area of Information Systems (machine learning, data mining, pervasive and ubiquitous computing, networking, health IT, HCC ):
 IEEE ICDM, ICDE, PerCom, ICML,
 ACM KDD, SenSys, IPSN, Ubicomp, MobiSys, BuildSys, CHI
Research Papers:
Approval Voting and Incentives in Crowdsourcing; Nihar Shah, Dengyong Zhou, Yuval Peres; ICML 2015
Smart Devices are Different: Assessing and Mitigating Mobile Sensing Heterogeneities for Activity Recognition; Allan Stisen, Henrik Blunck, Sourav Bhattacharya, Thor Siiger Prentow, Mikkel Baun Kjærgaard, Anind Dey, Tobias Sonne, and Mads Moller Jensen, In Proc. 13th ACM Conference on Embedded Networked Sensor Systems (SenSys 2015), Seoul, Korea, 2015
Student Support Services: UMBC is committed to eliminating discriminatory obstacles that disadvantage students based on disability. Student Support Services (SSS) is the UMBC department designated to receive and maintain confidential files of disabilityrelated documentation, certify eligibility for services, determine reasonable accommodations, develop with each student plans for the provision of such accommodations, and serve as a liaison between faculty members and students regarding disabilityrelated issues. If you have a disability and want to request accommodations, contact SSS in the Math/Psych Bldg., room 213 or at 4104552459. SSS will require you to provide appropriate documentation of disability. If you require accommodations for this class, make an appointment to meet with me to discuss your SSSapproved accommodations.
Academic Integrity: Cheating in any form, will be subject to discipline according to university regulations. Projects that contain plagiarized materials will receive an automatic letter grade of ‘F’. Multiple violations will be handled according to university regulation. Please refer to Academic Integrity for more information.