Fall 2017 Term

Note: Unless otherwise specified, all courses listed are worth 3 credit hours.

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Department: Computer Science

Class: CSCI B505 Applied Algorithms
Section: 33026
Instructor: Funda Ergun
Synopsis: The course studies the design, implementation, and analysis of algorithms and data structures as applied to real world problems. Topics include divide-and-conquer, optimization, and randomized algorithms applied to problems such as sorting, searching, and graph analysis. Students will learn about trees, hash tables, heaps, and graphs.

Class: CSCI B551 Elements of Artificial Intelligence
Section: 33013
Instructor: David Crandall
Synopsis: Introduction to major issues and approaches in artificial intelligence. Principles of reactive, goal-based, and utility-based agents. Problem-solving and search. Knowledge representation and design of representational vocabularies. Inference and theorem proving, reasoning under uncertainty, and planning. Overview of machine learning.

Class: CSCI B649 Privacy & Security in the IoT
Section: 33032
Instructor: Jean Camp
Synopsis: Security and privacy lapses in the Internet of Things can cause real and significant harm to people, their pets, and their homes. Computer security and privacy for an IoT ecosystem is fundamentally important and challenging. From a human-centered design perspective, complex issues arise when designing technologies for a diverse collection of stakeholders, including vulnerable populations such as children and those using in-home care technologies. From a technical perspective, security and privacy are challenging not only because of the properties of IoT devices themselves but also due to risks that emerge only when technologies are combined in unexpected ways. IoT devices will be pervasive, and may have very constrained computational, communications, and energy resources. Meeting these challenges requires a large, interdisciplinary effort. A holistic approach to IoT security and privacy integrates human-computer interaction, network security, cryptography, and pervasive computing. The translation layer requires an undertsanding of people’s privacy and security requirements and the ability to express these as cryptographically enforced data controls.

Department: Information and Library Science

Class: ILS Z639 Social Media Mining
Section: 32775
Instructor: Vincent Malic
Synopsis: This course provides a graduate-level introduction to social media mining and methods. It offers hands-on experience mining social data for social meaning extraction (focusing on sentiment analysis) using automated methods and machine learning technologies. We will read, discuss, and critique claims and findings from contemporary research related to SMM.

Department: Informatics

Class: INFO I520 Security for Networked Systems
Section: 14127
Instructor: Raquel Hill
Synopsis: This course is an extensive survey of system and network security. Course materials cover the threats to information confidentiality, integrity and availability, and the defense mechanisms that control such threats. It provides the foundation for more advanced security courses and hands-on experiences through course projects.

Class: INFO I523 Big Data Applications and Analytics
Section: 13310
Instructor: Gregor Von Laszewski
Synopsis: The Big Data Applications & Analytics course is an overview course in Data Science and covers the applications and technologies (data analytics and clouds) needed to process the application data. It is organized around this rallying cry: Use Clouds running Data Analytics Collaboratively processing Big Data to solve problems in X-Informatics.

Class: INFO I525 Organizational Informatics & Economics of Security
Section: 33034
Instructor: Jean Camp
Synopsis: Security technologies make explicit organizational choices that allocate power. Security implementations allocate risk, determine authority, reify or alter relationships, and determine trust extended to organizational participants. The course begins with an introduction to relevant definitions (security, privacy, trust) and then moves to a series of timely case studies of security technologies.

Class: INFO I535 Management, Access, and Use of Big and Complex Data
Section: 33630
Instructor: Inna Kouper
Synopsis: Data is abundant, offering potential for new discovery along with economic and social gain. But data has its difficulties. It can be noisy and inadequately contextualized. There can be too big a gap from data to knowledge, or due to limits in technology or policy not easily combined with other data. This course will examine the underlying principles and technologies needed to capture data, as well as clean, contextualize, store, access, and trust it for a repurposed use. Specifically we will cover 1) distributed systems and database concepts underlying noSQL and graph databases, 2) best practices in data pipelines, 3) foundational concepts in metadata and provenance plus examples, and 4) developing theory in data trust and its role in reuse.

Class: INFO I590 Applied Data Mining
Section: 33631
Instructor: Mehmet Dalkilic
Synopsis: TBA

Class: INFO I590 Applied Data Mining
Section: 33632
Instructor: Joanne Luciano
Synopsis: The aim of the Applied Data Science course is to provide the skills needed to apply data science principles on real world applications at every stage in the data science workflow. The course is organized around each stage covering the algorithms, best practices, and evaluation criteria. Both good and bad application examples will be discussed to help the student develop an intuition and deeper understanding of the choice of algorithm for the data, and the development of the best practices and methods for evaluating results of different approaches. Students will learn Tableau and use it to to visually analyze and report data.

Class: INFO I590 Data Science for Drug Discovery
Section: 33633
Instructor: Joanne Luciano
Synopsis: With exploding healthcare costs, greater longevity and the widespread health challenges of diabetes, obesity, cancer and cardiovascular disease, today's medicine and healthcare will be a primary scientific and economic focus for the remainder of this century. Informatics and big data promise an understanding of health, disease and treatment on a scale never before imagined. This course will address the big data techniques that are being used in the drug discovery, healthcare and translational medicine domains. Some specific topics covered will include large-scale, integrated molecular datasets; cheminformatics and bioinformatics in a big data domain; storing and data mining of electronic medical records; visualization and mapping of diseases; bridging the clinical and molecular; smart devices for smart health; and data mining for healthcare economics.

Class: INFO I590 Data Science On-Ramp
Section: 33634
Instructor: Ying Ding
Credit Hours: 1 - 3
Synopsis: A course dealing with self-paced modules to build and strengthen core competencies necessary for Data Science curriculum. Individual lessons vary from beginner to intermediate and will cover C++, MongoDB, R, Java, Python, Tableau, SQL, Hadoop/MapReduce, Spark, Scala, Github, Web Scraping, and Text Mining (NLP). If you would like descriptions of each lesson and how these will be mapped to credit, please consult Professor Ying Ding for more information.

Class: INFO I590 Data Semantics
Section: 13972
Instructor: Ying Ding
Synopsis: The class explores the technologies of the Semantic Web by examining the application of technologies to WWW information delivery and the principles of formal logic and computation guiding their development.

Class: INFO I590 Data Visualization
Section: 14120
Instructor: Yong-Yeol Ahn
Synopsis: From dashboards in a car to cutting-edge scientific papers, we extensively use visual representation of data. As our world becomes increasingly connected and digitized and as more decisions are being driven by data, data visualization is becoming a critical skill for every knowledge worker. In this course we will learn fundamentals of data visualization and create visualizations that can provide insights into complex datasets.

Class: INFO I590 Python
Section: 33636
Instructor: Vel Melbasa
Synopsis: This course provides a gentle yet intense introduction to programming with Python for students who have little or no prior experience in programming. Python, an open-source language that allows rapid application development of both large and small software ystems, is object-oriented by design and provides an excellent platform for learning the basics of language programming. The course will focus on planning and organizing programs, and developing high quality working software that solves real problems.

Class: INFO I591 Graduate Internship
Section: 14072, 14073
Instructor: David Wild
Credit Hours: 0 - 6
Synopsis: Students gain professional work experience in an industry or research organization setting, using skills and knowledge acquired in Informatics course work. May be repeated for a maximum of 6 credit hours.

Class: INFO I699 Independent Study
Section: 14074, 14075
Instructor: David Wild
Credit Hours: 1 - 3
Synopsis: Independent readings and research for MS students under the direction of a faculty member, culminating in a written report.

Department: School of Public and Environmental Affairs

Class: SPEA V506 Statistical Analysis for Effective Decision-Making
Instructor: TBA
Synopsis: This course provides graduate-level instruction in the application of statistical analysis to issues in public and environmental affairs and related fields. It is designed to assist students in learning the methods by which statistical analysis is carried out, as well as the basic theory that enables and constrains the application of statistics to real world data. The course emphasizes practical aspects of applying such methods, appropriately interpreting the results of these statistical analysis tools, and gaining a meaningful understanding of how statistical analysis can be misused or erroneously executed (either intentionally or unintentionally). As such, the course will address descriptive statistics, statistical inference, the nature of random variables, sampling distributions, point and interval estimation of parameters (mean, standard deviation, etc.), hypothesis testing, analysis of variance, and bivariate and multivariate regression. Although these are traditional topics for an introductory statistics course, the emphasis in V506 will be on appropriately applying these techniques and extracting meaningful information from unstructured data. Use of computer tools for carrying out statistical analysis (primarily SAS) will also be a major emphasis.

Department: Engineering

Class: ENGR E599 Cloud Computing
Section: 35087, 35089
Instructor: Geoffrey Fox
Synopsis: The course covers all aspects of the cloud architecture stack, from Software as a Service (large-scale biology and graphics applications), Platform as a Service(MapReduce (Hadoop), Iterative MapReduce (Twister) and NoSQL (HBase)), to Infrastructure as a Service (low-level virtualization technologies. At the end of this course, you will have learned key concepts in cloud computing and enough programming to be able to solve data analysis problems on your own.

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