Spring 2024 Tu 7:00pm - 9:40pm Josef Schaff
Focuses on applying machine learning techniques to cybersecurity, and includes labs to be done independently, as well as an overview of the latest machine learning algorithms and their application to cyber. A brief overview of which techniques ... should be applied to particular cyber problems will be provided, and the course culminates in students researching the latest applications of Machine learning to cyber, allowing the students to each develop a niche of expertise in that specific subtopic. As such, the students should be increasingly employable in their area of cyber expertise by industries searching for solutions to their cyber problem space.

Data science encapsulates the interdisciplinary fields for creating data-centric products and applications towards specific needs. This course’s goal is to provide an introduction to data science from the perspective of security expert. Due ... to increased sophistication of targeted attacks and insufficiencies of traditional passive security measures in mitigating persistent targeted hackers, data science with the use of Big Data techniques is considered as a promising alternative for identifying threats and attacks. This specialized field includes (a) systems for storing and processing massive data sets; (b) data analytics for extracting information from such data sets; (c) security for assessing the cyber attacks.

Spring 2024 Th 1:00pm - 3:40pm Karthikeyan Chandrashekar
Software engineering concepts, methods, and practices important to both the theorist and the practitioner will be covered. The entire range of responsibilities expected of a software engineer are presented. The fundamental areas of requirements ... development, software design, programming languages, and testing are covered extensively. Sessions on supporting areas such as systems engineering, project management, and software estimation are also included.
Prerequisite: Competency in one programming language and must have completed an undergraduate software engineering course or permission of course instructor.

Introduction to embedded systems design and evaluation: requirements, specification, architecture, hardware and software components, integration and performance evaluation. Topics include instruction sets, CPU, embedded computing platform, ... program design and analysis, operating systems, hardware accelerators, multiprocessors, networks, and system analysis. Real-life embedded systems design examples will be used throughout the course to illustrate these concepts. Selective hot topics such as low power embedded systems design and hardware security will also be discussed.
ENEE 150 or equivalent (recommended), ENEE 244 or equivalent (required), and ENEE 350 or equivalent (required).

Exploration of how such a variety of devices can use a big range of technologies to connect seamlessly to each other. In the second half of the course we translate the basic knowledge of the protocols to more hands-on exercises in containerization ... (Docker) and at the end we give an introduction to Kubernetes, that is an open-source system for automating deployment and management of containerized applications.
Prerequisite: ENPM602

Spring 2024 Th 7:00pm - 9:40pm Jerry Wu
Machine learning may be used to greatly expand the capabilities of robotic systems, and has been applied to a variety of robotic system functions including planning, control, and perception. Adaptation and learning are particularly important ... for development of autonomous robotic systems that must operate in dynamic or uncertain environments. Ultimately we would like for the robots to expand their knowledge and improve their own performance through learning while operating in the environment (on-line and/or lifelong learning). Robot Learning covers the application of learning techniques including Reinforcement Learning, Learning from Demonstration, and Robot Shaping that may be used with a variety of machine learning paradigms for which data is used to generate (through induction) models that are then used by the robot to perform tasks. A wide variety of paradigms are available to generate models (e.g., CMAC, KNN, MLP, lazy learning, LWR, RBF, deep networks). These learning techniques and paradigms are then combined with traditional robotic control approaches (e.g., motor schema, behavior-based, direct and inverse methods) to create controllers to control the robots while operating in real-world environments. This graduate course will explore the application of machine learning techniques, paradigms, and control design to robotic systems, focusing primarily on key useful representations and model building techniques for application in non-stationary robotic systems.
Formerly: ENPM808F.

Spring 2024 F 2:00pm - 4:40pm Dharmalingam Ganesan
This course teaches the fundamentals of secure programming in C. An in depth discussion on various security vulnerabilities (e.g., buffer overflows) in C applications will be taught with hands-on demo of concepts during the class. Students ... will learn how a C program runs “under-the-hood”. The course will teach nitty-gritty of C programs by analyzing at the assembly level. The course discusses best practices (e.g., coding standards) and design principles for secure programming so that security can be built-in during design time. In addition to assignments, students are required to present papers related to this course.

Spring 2024 W 7:00pm - 9:40pm Majid Raissi-Dehkordi
This course is an introduction to the diverse field of cryptography and network security. We start by an introduction to the principles of cryptography and review common encryption standards as the basis for many security protocols. We review ... current standards for cryptography, hash functions, message authentication, digital signatures, as well as certificates and key management. The second part of the course reviews some of the well-known security protocols at different network layers (Application, Transport, IP, and MAC) as examples of how fundamental concepts are utilized for providing the corresponding security requirements. The course combines theoretical discussions and hands-on assignments to provide the students with a longer lasting understanding of the concepts.
Prerequisite: An operating systems and/or network protocol course or equivalent. Formerly: ENPM808N.

Provides an in-depth review of the Internet with a focus on the end-to-end effects of technologies and protocols that operate in different layers. All protocols and technologies are covered in a holistic framework with an emphasis on their ... effect on the network and application performance. The course also includes a brief introduction of more modern concepts in the field of networking such as SDN and NFV to encourage deeper study of those topics.
Note: Previously offered as ENPM808A

This course provides in-depth understanding of software reverse engineering concepts and hands-on training with reverse engineering tools, including disassemblers, decompilers, and code analyzers. Students will become familiar with both low-level ... software and the x86 instruction set through binary reversing sessions. This course also provides insights into many subjects such as system security, source code analysis, software design, and program understanding that will be beneficial in a variety of fields.
Prerequisite: ENPM691 and CMSC106

The course will cover the challenges and rewards from harvesting renewable energy offshore. The phenomenal growth of data assets (both within organizations and publicly) combined with advances in technology (in cloud computing and machine ... learning) have allowed organizations to leverage their data assets to enhance their decision making processes in order to achieve their missions. In addition, the availability of low-cost computing resources and open-source tools have accelerated this process. This course focuses on practical methods to extract meaningful insights from (internal and external) data sources and to communicate those findings to stakeholders so they can be incorporated in operational work processes to achieve organizational goals. Students will use relevant tools, technologies and approaches for data manipulation, statistical analysis, predictive modeling, and visualization in case studies/projects. Cloud computing and distributed computing platforms including architectural approaches in implementing a Big Data Platform for decision making will be covered. Students will use analytics tools and scripting languages for the implementation of projects. Some example languages/tools introduced are: Python and Tableau. Prior knowledge of scripting language such as Python or R will be helpful – but not required.

This course provides students with an understanding of the fundamental concepts of artificial intelligence and machine learning and explores applications of these concepts to real world problems.  We will review the topics of design of ... intelligent agents, logic and symbolic reasoning, probabilistic reasoning and decision making, knowledge representation, problem solving techniques, and deep learning. The students will be assigned to apply AI and machine learning techniques in various areas of their interest like cybersecurity, robotics, advanced electronics, software verification, database validation, data cleaning and imputation, product design, environmental engineering, healthcare, drug discovery, etc.  Upon completion of the course students should be able to develop solutions to concrete problems and create models and tools based on AI and deep leaning techniques.
Prerequisites: There are no formal requirements for this class although students should be familiar with basic concepts in probability theory and statistics, linear algebra, and have some coding skills (e.g., Python)

Spring 2024 M 4:00pm - 6:40pm Yantian Zha
Drawing analogy to humans cognitive intelligence is beneficial for designing smarter robots. This course will cover the fundamentals of human cognitive intellegnce and the connections between robotics and cognitive science. Students will design ... cognitive robotics learning architectures and deploy them in a simulator like Meta-Habitat 2.0.

Communication & Signal Processing
This course covers the fundamental aspects of Internet of Things (IoT) including IoT architecture, network protocols, data management and access technologies, IoT security threats and technologies

This course will introduce methods for analyzing and understanding the structure and function of networks, including social networks, web graphs, and sensor networks. The course will introduce students to the math and science of network analysis. ... Through real world examples, including analysis of their own networks, students will develop skills for describing and understanding the structure, patterns, and functionality of networks. Students will read classic and cutting edge articles and books about these topics and discuss their applicability to various network types. The class will culminate with a capstone project in which students will apply the analysis methods they have learned to understanding a particular question about a network they choose.

Communications and Signal Processing
The course will teach the concepts and techniques for Software Defined Networking (SDN), including an overview of available controllers, switches, and tools. More specifically, the course will cover a review of basic networking concepts, motivation ... and use cases for SDN, general SDN architecture and building blocks, Network Functions Virtualization (NFV), SDN protocols, and existing SDN applications.

Computer Engineering
Spring 2024 Th 4:00pm - 6:40pm Emre Gunduzhan
The course will teach the algorithms, heuristics, and code development skills in solving numerous sorting, graph theory, string processing, and job scheduling problems. It provides both a broad coverage of basic algorithms and data structures ... and in-depth coverage on selected topics. Moderate to heavy programming in C is expected. Through this study the students will improve their problem solving skills via various algorithm development examples and exercises.