LTU’s MSAI pairs a rigorous computer-science core with hands-on work in machine learning, deep learning, data mining, and pattern recognition, delivering a curriculum in lockstep with the latest AI breakthroughs. Students move from theory to building functional prototypes at lightning speed, gaining the analytical depth and production‑grade coding skills needed in today’s fast‑moving tech sector.
Continuous AI research and innovation is driving the explosive growth of industries such as:
LTU’s MSAI program targets the competencies that industry chases, including AI‑focused software development, digital signal processing, cybersecurity, and embedded networking.
As a student, you’ll benefit from daily exposure to faculty actively shaping AI research, enabling real-time insight into emerging techniques. Coursework is built around real-world challenges, so you graduate ready to thrive at the front line of AI innovation.
This option requires eighteen (18) credit hours of core courses plus three (3) credit hours of graduate project and nine (9) credit hours of specialization for a total of thirty (30) credit hours.
Available specializations include:
This option requires twenty one (21) credit hours plus a nine (9)-credit-hour thesis for a total of thirty (30) credit hours. The student, in consultation with his or her thesis advisor, proposes a thesis topic by submitting the “Petition for a Master’s Thesis” form that describes the research topic in detail and presents the research plan. The thesis proposal must be successfully presented to the student’s thesis committee before the master’s thesis credits are elected. Once the thesis is accepted, the student can take any combination of EEE 6911, EEE 6912, and EEE 6913, to add up to the nine thesis credits. Once the thesis is completed, the student must successfully defend it before his or her thesis committee. Students must submit at least one conference or journal paper successfully prior to defending their thesis.
Course Name
Course #
Credits
This course focuses on writing maintainable and extensible engineering systems code development. Topics include: smart software encryption and cybersecurity development in autonomous vehicles, physical systems semantic networks, frames, pattern matching, deductive inference rules, case-based reasoning, and discrimination trees. Project-driven. Substantial programming assignments. Including interactive programming with industrial automation hardware and software.
EEE5513
3
The objective of the course is to study, understand, and practice the concepts of machine learning and pattern recognition. The course will cover the basic aspects of pattern recognition and machine learning such as different approaches to feature selection, classification methods, interpolation methods, and techniques of machine learning performance evaluation. In the end of the course the students will be able to implement all aspects of pattern recognition to create a working machine learning system that will solve a real-life pattern recognition problem.
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
Covers a new or specialized topic in Mechanical Engineering for which there is strong faculty and student interest, but is not covered in other courses. Credit hour is indicated by the last digit of the course number.
EME5983
3
This course focus on system design using AI methods in engineering application. Topic in: shaft encoders, actuators, robot coordinate systems, kinematics, path control, sensors, robot vision, and design of robot interfaces. Substantial programming assignments. Including interactive programming with industrial automation hardware and software.
EEE5563
3
This course focus on system design using AI methods in engineering application. Topics in: AI in robotics, electrical equipment design, printed circuit board design, electrical automation control, AI programming languages, intelligent vision and imaging systems, database search methods, logic and deduction using predicate calculus. Expert system design with applications to robots. Substantial programming assignments. Including interactive programming with industrial automation hardware and software.
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
This course introduces students to the design of mechatronic systems through a combination of lectures and hands-on laboratory experiments. Lecture and laboratory topics include basic electronics, sensors, actuators, and microprocessor implementation. Following the structured laboratories, teams of students will design and build a mechatronic system to complete a designated task within a designated budget.
MRE5183
3
State space realization of transfer functions, canonical forms, fundamental and state transition matrices, introduction to optimal control, quadratic performance indices, observers, Liapunov stability theory.
MRE5323
3
Course Name
Course #
Credits
Must have departmental approval. Discrete time mathematics, Z-transforms, sampling rates, zero and first-order hold, time delays, system stability, continuous and discrete time systems, interfacing, computer control implementation concepts, state space realization. Lecture 4 hours.
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
Brain-inspired Deep Learning (DL) is a subfield of machine learning that trains neural network based models to perform human-like tasks, such as
identifying images, recognizing speech, or making predictions. A DL system is trained rather than explicitly programmed. To train a DL system, a set of example data as well as the answers expected from the data are used. This course will cover a range of topics from dense networks, Convolutional Neural Networks (CNN), recurrent neural networks and long short-term memory (LSTM), and Generative Adversarial Networks (GAN). Students will apply deep learning to real-world problems as class projects.
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
Current trends and technology in computer science will be presented to Freshman and Sophomores to provide opportunities to begin to study and research a specialized topic. Topics will be decided by the faculty who are teaching.
MCS5993
3
Course not found.
MRE5XX3
3
Course Name
Course #
Credits
Design of baseband and passband digital communication systems. Modulation techniques including PAM, QAM, PSK, FSK, and spread spectrum. Optimal demodulation techniques and their performance. Analysis, evaluation and design of integrated circuits for communication applications.
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:
Use Your Cell Phone as a Document Camera in Zoom
From Computer
Log in and start your Zoom session with participants
From Phone
To use your cell phone as a makeshift document camera