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Artificial Intelligence (AI) is proven to be a key element in technological innovations in the ever-expanding digital age. As Computer utilization becomes more sophisticated, analyzing problems and programming solutions requires advanced AI algorithms to deal with Big Data such as machine learning, deep learning, data mining, and pattern recognition.
The MSAI degree allows students the opportunity to focus on computer science skills combined with applications in numerous scientific areas to provide students a competitive edge in today’s advance technological landscape.
Continuous AI research and innovation is driving the explosive growth of industry verticals such as:
Courses are offered in the areas of Connected Vehicle Technologies, Deep Learning for Engineers, Software Development for AI, Digital Signal Processing, Computer Network Cyber Security, and Embedded Networking. These courses give students the opportunity to build on what they have already learned throughout their academic and industrial careers, allowing them to be at the forefront of technological innovation.
Theoretical knowledge is blended with hands-on experience in implementing practical applications.
LTU Faculty are expert in advanced areas such as, autonomous vehicles, big data mining, computer vision, and natural language processing, machine learning, math modeling and medical robotics (or robotics in healthcare).
The MSAI program consists of 30 credit hours. The seven courses (21 credits) consist of six lecture courses and one graduate project. The lecture courses provide the students with AI knowledge, and the graduate project will be managed as a directed study course, that can be composed of student teams of two or three members guided by the faculty. Students may enroll in the graduate project after the completion of the six (6) core courses and concurrently with the specialization courses.
The students will select three courses (nine credits) from a specialization. The program will start with specializations in “Connected Vehicles” from the ECE Department and “Data Science” from the MCS Department. Future planned specializations are included in the list below to communicate the long-term goals of the program.
The graduate project will serve as a practicum and a practical excursion building AI application or a graduate project in AI.
Course Name
Course #
Credits
Course not found.
EEE5513
3
Course not found.
MCS5623
3
This course focus on Sampling theory and sampling hardware, Z-transform, Discrete Time Fourier Transform, architecture of VLSI digital signal processors. Design and implementation of real time polynomial, FIR, IIR, and adaptive filters, spectral analysis with DTFT will be dealt. Filter realization techniques, Direct I, Direct II, Canonical, Parallel form. Design of DSP application in communication and digital control. Substantial programming assignments. Including interactive programming with industrial automation hardware and software.
EE5653
3
Beginning course on theory of computation. Regular languages, finite automata, context-free language, Turing Machine, Chomsky hierarchy, applications to parsing. Lecture 3 hrs.
MCS5243
3
This course introduces the fundamental concepts & methods of knowledge representation, perception, reasoning, problem solving, data-mining, and machine learning in Artificial Intelligence (AI). Topics covered include Knowledge-Based Systems, Rule-Based Expert Systems, Uncertainty Management, Fuzzy Systems, Artificial Neural Networks, Evolutionary Computation, Semantic Web, and Autonomous Robotics.
MCS5323
3
This course introduces a machine learning technique called deep learning and its Electrical Engineering applications, as well as core machine learning concepts such as data set, evaluation, overfitting, regularization and more. Topics in: Real-time decisions in autonomous vehicles, warning systems, radar, LiDAR sensor fusion. Covers neural network building blocks: linear and logistic regression, followed by shallow artificial neural networks and a variety of deep networks algorithms and their derivations. Including interactive programming with industrial automation hardware and software.
EE5523
3
Building on a first undergraduate course in data structures, this course contains a deeper analysis of the design of efficient algorithms on data structures for problems in sorting, searching, graph theory, combinatorial optimization, computational geometry, and algebraic computation. Topics covered in the course include divide-and-conquer, dynamic programming, greedy method, and approximation algorithms.
MCS5803
3
Graduate Project
MCS/EEE/MRE/EME
6XX3
3
Total Credits:
30
Course Name
Course #
Credits
Course not found.
EME5983
3
Course not found.
EEE5563
3
Course not found.
EEE5553
3
This course introduces theories, algorithms, techniques, practical issues, and tools to develop & engineer software for intelligent autonomous robotics systems with ROS (Robot Operating System) software development environment. ROS has a large open source community and is becoming widely adopted in research, industrial, and autonomous vehicle applications. Covered topics include sensor data processing, machine vision, mobile robot control, localization, navigation, mapping, state machines, human-robot interaction/interfaces, robot communication, and 3D modeling and simulation with Gazebo. The course will also give students experience using Git, Linux, and various C++/Python tools and frameworks. Machine learning and deep learning technologies for autonomous vehicles will also be introduced.
MCS5403
3
Course not found.
MRE5183
3
Course not found.
MRE5323
3
Course Name
Course #
Credits
Course not found.
EEE5533
3
This course focuses on understanding the fundamentals and applications of digital image analysis (or computer vision) techniques including 2-D and 3-D to solve real world applications. Vision systems, image formation, edge detection, image segmentation, texture, representation and analysis of two-dimensional geometric structures, and representation and analysis of three-dimensional structures. Substantial programming assignments. Including interactive programming with industrial automation hardware and software.
EEE5353
3
Course not found.
EEE6523
3
Course Name
Course #
Credits
Course not found.
MCS5713
3
With an objective to study, understand, and practice the concepts of data mining using social network data. The course will cover the basic aspects of data mining such as different approaches to classification, regression, segmentation, text analysis, recommendation systems, etc. The aim is to develop skills in obtaining data from social network, analyzing it and visualizing it.
MCS5723
3
Course not found.
MCS5993
3
Course not found.
MRE5XX3
3
Course Name
Course #
Credits
Course not found.
EEE5443
3
This course focuses on basic understanding of the theoretical foundations and applications of artificial embedded neural networks. Network design and topology, hardware devices, and communication/data exchange protocols needed to connect and exchange information between embedded systems. Substantial programming assignments. Including interactive programming with industrial automation hardware and software.
EEE5453
3
Local asynchronous communication; extending LANs modems, repeaters, bridges; switches; packet switches; service paradigms; protocols and layering; binding protocol address; network management software; network security-filtering and firewalls. Course contains lecture and laboratory sections.
EEE5463
3
This course introduces students to the strategic and operational uses of information systems. The use of information systems is examined for achieving and maintaining competitive advantage, as well as managerial issues concerning the development, implementation, and management of enterprise information systems. Case studies address the impact of information systems on the organization, the challenges involved in managing technological change in organizations, and the impact of emerging technologies. Students will develop a socio-technical perspective on the use of information systems to solve real-world problems.
INT6043
3
As networks continue to grow and as computing becomes more and more ubiquitous, today’s IT Managers need to have a thorough understanding of security and the risks associated when inappropriate security exists. Students will explore basic security concepts, principles and strategy, how to develop and manage IT security program and how to strategize and plan an IT architecture. Students will also discuss other IT security issues as it relates to current market trends.
INT7223
3
Admission to the MSAI program as a regular graduate student requires the demonstration of high potential for success based on the following:
*Applicants must have earned a baccalaureate degree from an accredited U.S. institution –or– a non-U.S. degree equivalent to a four-year U.S. baccalaureate degree from a college or university of government recognized standing.
**A Bachelor of Science degree in Electrical and Computer Engineering or Mathematics and Computer Science (or technical related field) (minimum GPA of 3.0)
Students with a GPA between 2.8 and 3.0 may be admitted on a provisional basis. They will be evaluated for official graduate student status upon completion of pre-core courses, if necessary, and 12 semester hours of required electrical and computer engineering graduate coursework at Lawrence Tech. This evaluation will be conducted by the program director and the Graduate Admissions Committee. Students are notified of their status within two weeks of completion of the minimum required hours.
Students with a Bachelor of Science degree in a field other than electrical or computer engineering or mathematics and computer science who have a GPA of at least 3.0 may be admitted on a provisional basis. These students must satisfy all prerequisite requirements before they can be granted official graduate status. The program director and the Graduate Admissions Committee decide what the prerequisite requirements are on a case-by-case basis.
In order to continue in the MSAI program, students must have a cumulative graduate GPA of at least 3.0 out of 4.0. A student whose cumulative GPA falls below 3.0 at any time during their tenure will be placed on academic probation and must consult with the program director regarding continuation in the program. After admission to the MSAI program, students must meet with their academic advisor prior to class registration, each semester, to discuss and select plan of study. The final plan of study and selection of specialization must be submitted no later than by the time of completion of the lecture courses in the core curriculum.
Candidates for the MSAI degree must complete 30 semester hours within the MSAI curriculum. In the semester prior to their anticipated graduation, candidates for the MSAI degree will complete the form Petition To Graduate. The program director will then review the petition and articulate remaining degree requirements. Artificial Intelligence Advisor/Director All students should have an advisor/director-approved Plan of Work. Contact George Pappas, Director of Artificial Intelligence, at 248.204.2559 or gpappas@ltu.edu, to set up an appointment. Students are required to maintain an overall and program GPA of 3.0.
Students will:
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Log in and start your Zoom session with participants
From Phone
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