- 1 Schedule
- 2 Additional Course Information
- 3 CS 495/595 Course Information
- 4 CS 795/895 Course Information
See Recently Offered Courses for more information on CS 532, CS 541, CS 562, CS 725, CS 734
Click the image below for a larger PDF version of the schedule:
Additional Course Information
CS 432/532 – Web Science – Alexander Nwala
Description: The Web has fundamentally changed how we learn, play, communicate, and work. Although Web Science is interdisciplinary by nature, this course will be focusing mainly on the computing aspects of the Web: how it works, how it is used, and how it can be analyzed.
We will examine a number of topics including:
- Web architecture, characterization, and analysis
- Web Crawling and Web archiving,
- Social networks and Collective intelligence,
- Search engines and Web mining,
- Collective Intelligence and Recommender Systems
- Clustering and Classification Algorithms, etc.
Tools we will learn to use include:
- Twitter API
Spring 2018 course page: https://anwala.github.io/lectures/cs532-s18/
CS 734/834 – Intro to Information Retrieval (Search Engines) – Dr. Wu
Description: This class will explore the theory and engineering of information retrieval in the context of developing web-based search engines. The course will explore issues related to crawling, ranking, query processing, retrieval models, evaluation, clustering, machine learning, and other aspects related to building search engines. The course will also cover recently established ranking algorithms that incorporate semantic similarities, machine learning, and neural network methods, such as learning to rank and neural information retrieval. The class will feature several hands-on development and coding using tools such as Google Custom Search, ElasticSearch, as well as a theoretical exploration of the existing literature on these topics. Students must be comfortable with self-directed learning appropriate for an advanced graduate class.
Spring 2019 course page: http://fanchyna.wixsite.com/jianwu/cs734834-spring2019
CS 495/595 Course Information
Principles and Practices of Cyber Defense – Dr. Zhao
Description: This course is to help students gain a thorough understanding of vulnerabilities and attacks in systems and networks and learn cyber defense best practices. It covers fundamental security design principles and defense strategies and security tools used to mitigate various cyber attacks. The fundamental goals of this course are that students will acquire:
- A principled understanding of the basic physical and virtual architecture of the cyber domain, focusing on: the individual computer and program, the physical components and protocols of a network and the Internet, and the distributed client-server system that is the World Wide Web;
- Hands on experience with basic components of the physical and virtual architecture in the cyber domain and the ability to relate that experience to the larger system;
- A principled understanding of DoD’s Pillars of Cyber Security (Confidentiality, Integrity, Availability, Non-repudiation, Authentication), the inherent vulnerabilities of information systems that endanger these properties, defensive measures to ensure that information systems retain these properties, and offensive measures that can be used to violate these pillars; and
- Hands on experience with some basic offensive and defensive practices in the cyber domain, and the ability to relate that experience to new or more sophisticated attacks and defenses.
Lab work required.
- Identification of reconnaissance operations
- Anomaly/intrusion detection and identification
- Identification of command and control operations
- data exfiltration detection and prevention
- Identifying malicious code based on signatures, behavior and artifacts,
- Network security techniques and components
- Cryptography in cybersecurity
- Malicious activity detection
- System security architectures and concepts
- Defense in depth
- Trust relationships
- Distributed/Cloud and virtualization
Data Science with Python – Dr. Zubair
Description: The objective of this course is to introduce data science using Python programming language. The course will be a hands-on course and requires access to laptops/computers in the class. In the class, lectures will be interleaved with lab work. One of the components of this course is a class project where the students will apply the knowledge acquired during the course for a real application.
A tentative list of topics to cover in this course is given below.
- Basics of Python programming. Setting up the environment. Use of Python Notebook.
- Use of libraries like Numpy and Scipy, and Data manipulation using Pandas
- Computing basic statistics on data
- Working with higher dimensional data
- Working with streaming data such as financial data
- Plotting and visualization
- Introduction to basic machine learning models
Prerequisites: Elementary Statistics and Probability; Basic programming (any language): variables, data types and expressions, assignment, control-flow statements, functions, arrays, structs/records; and familiarity with Windows and Unix OS.
CS 795/895 Course Information
3D Image Deep Learning and Modeling – Dr. He
more info coming soon
Web Archiving Forensics – Dr. Nelson
The veracity of information on the web and social media is of increasing concern, especially in light of recent developments in
foreign disinformation campaigns to influence elections and questions about whether public figures actually said what may be attributed to them*. Digital information is easy to manipulate, copy, and delete, but web archives offer a trusted method for timestamping the appearance of web pages and their contents. We will investigate how web archives can be used to establish the priority of information, as well as how they can be hacked or used to obfuscate the provenance of falsified content. The class will consist of exploring web archiving mechanics and protocols, reviewing prior research, and class projects that implement and replicate current issues involving web archives.
This class is one of five classes that the Web Science and Digital Libraries Group is offering in Spring 2019:
Deep Learning in Medicine and Biology – Dr. Sun
The objective of this course is to introduce various deep neural networks (DNNs), and review the state-of-the-art literature related to DNNs in human medicine and related biology. The course consists of lectures, paper reviews (writing reviews, in-class student presentations and discussion), and projects. Lectures will serve as the vehicle for the instructor to introduce basic concepts and knowledge to students. Paper reviews are used to inform students of the latest research topics and techniques. A course project will be used for students to get profound hands-on experience by programming certain deep learning methods identified from the recent literature. The tentative topics that will be covered include:
Deep neural networks: convolutional neural network, residual network, recurrent neural network, long short-term memory network, generative adversarial network, autoencoder, etc.
Applications in patient classification: medical images, electronic health records, longitudinal data analysis
Applications in biological process: gene expression, protein secondary and tertiary structure, structure determination and cryo-electron microscopy, neuroscience, single-cell data
Human Computer Interaction – Dr. Jayarathna
This course is designed to introduce graduate students in computer science, topics to principles of and practices in human-computer interaction (HCI), an interdisciplinary area concerned with the study of the interaction between humans and interactive computing systems. Research in HCI looks at major cognitive and social phenomena surrounding human use of computers with the goal of understanding their impact and creating guidelines for the design and evaluation of software and physical products and services in industry.
In this course, we’ll study Human Computer Interaction (HCI) areas; including history and importance of HCI; design theories; modeling of computer users and interfaces; empirical techniques for task analysis and interface design; styles of interaction and future interaction techniques such as brain-computer interfacing (BCI).
We will also work on a semester-long team project (startup) to design, implement and evaluate a mainstream technology. This assignment helps foster an entrepreneurial spirit, which is at the heart of blending computer science, interaction design and HCI.
Spring 2019 course page: https://www.cs.odu.edu/~sampath/courses/s19/cs795/