Turn data into discovery and innovation into impact with LTU’s Master of Science in Artificial Intelligence (MSAI), where cutting-edge learning meets real-world application.
Turn data into discovery and innovation into impact with LTU’s Master of Science in Artificial Intelligence (MSAI), where cutting-edge learning meets real-world application.
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
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.
This option requires twenty one (21) credit hours plus a nine (9)-credit-hour thesis for a total of thirty (30) credit hours. 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.
Engineer autonomous systems, machine vision, and real-time embedded controls.
Available Courses Include:
Design intelligent transportation technologies and mobility systems of tomorrow.
Available Courses Include:
Turn big data into actionable insights using neural networks and advanced analytics.
Available Courses Include:
Protect digital ecosystems by integrating AI into secure network defense and system monitoring.
Available Courses Include:
AI Consultant
Computer and Information Research Scientist
Senior Product Manager
Senior Machine Learning Engineer
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