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Artificial Intelligence

Master of Science

Home » All Programs » Artificial Intelligence
Artificial Intelligence (AI) is proven to be a key element in technological innovations in the ever-expanding digital age.

» Program Overview

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:

  • Automotive
  • Retail
  • Manufacturing
  • Healthcare
  • Finance
  • Software Development
Why LTU?

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).

 

Flowcharts/Summary Sheets

Master of Science in Artificial Intelligence Flowchart

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» Curriculum

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.

  • Connected Vehicles consisting of Connected Vehicle Technologies, Computer Vision, and Advanced Deep Learning
  • Data Science consisting of Machine Learning and Neural Networks, Social Network Mining, Text Mining and Analytics, and Applied Machine Learning
  • Robotics and Sensors consisting of Bioinspired Robotics, Interface and Control of Robotics, Application of Artificial Intelligence, Intelligent Robotics with ROS
  •  consisting of Computer Network Cyber Security, Embedded Networking, Computer Networking, Cybersecurity, Management Information Systems

The graduate project will serve as a practicum and a practical excursion building AI application or a graduate project in AI.

Core Courses (21 credit hours) Complete six (6) lecture courses
COURSE NAME COURSE # CREDITS
Soft Dev for AI
EEE5513
3
Machine Learning
MCS5623
3
Digital Signal Processing
EEE5653
3
Theory of Computation
MCS5243
3
Artificial Intelligence
MCS5323
3
Deep Learning for Engineers
EEE5523
3
Algor. Design & Analysis
MCS5803
3

Total Credit Hours

21

COURSE NAME COURSE # CREDITS
Special Topics in ME
EME5983
3
Interfacing and Control Robots
EEE5563
3
Application of AI
EEE5553
3
Intelligent Robotics with ROS
MCS5403
3
Mechatronic Systems I
MRE5183
3
Modern Control Systems
MRE5323
3

Total Credit Hours

18

COURSE NAME COURSE # CREDITS
Connected Vehicles
EEE5533
3
Computer Vision
EEE5353
3
Adv Deep Learning for Engineer
EEE6523
3

Total Credit Hours

9

COURSE NAME COURSE # CREDITS
Deep Learning
MCS5713
3
Social Network Mining
MCS5723
3
Topics in Computer Science
MCS5993
3
Applied Machine Learning
MRE 5xx3
3

Total Credit Hours

12

COURSE NAME COURSE # CREDITS
Computer Networking CS
EEE5443
3
Embedded Networking
EEE5453
3
Computer Networking
EEE5463
3
Mgt. Info. Systems
INT6043
3
Cybersecurity
INT7223
3

Total Credit Hours

15

COURSE NAME COURSE # CREDITS
Graduate Project – AI
MCS/EEE/MRE/EME 6xx3
3

Total Credit Hours

3

» Awards + Accreditation

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Best Engineering Graduate Schools

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» Document Viewer

Use Your Cell Phone as a Document Camera in Zoom

  • What you will need to have and do
  • Download the mobile Zoom app (either App Store or Google Play)
  • Have your phone plugged in
  • Set up video stand phone holder

From Computer

Log in and start your Zoom session with participants

From Phone

  • Start the Zoom session on your phone app (suggest setting your phone to “Do not disturb” since your phone screen will be seen in Zoom)
  • Type in the Meeting ID and Join
  • Do not use phone audio option to avoid feedback
  • Select “share content” and “screen” to share your cell phone’s screen in your Zoom session
  • Select “start broadcast” from Zoom app. The home screen of your cell phone is now being shared with your participants.

To use your cell phone as a makeshift document camera

  • Open (swipe to switch apps) and select the camera app on your phone
  • Start in photo mode and aim the camera at whatever materials you would like to share
  • This is where you will have to position what you want to share to get the best view – but you will see ‘how you are doing’ in the main Zoom session.