Graduate Certificate Programs

Computational Intelligence

Program Description:
Recent advances in information technology and the increased level of interconnectivity that society has achieved through Internet and broadband communication technology created systems that are very much different. The world is facing an increasing level of systems integration leading towards Systems of Systems (SoS) that adapt to changing environmental conditions. The number of connections between components, the diversity of the components and the way the components are organized can lead to different emergent system behavior. Computational intelligence tools are an integral part of these systems in enabling adaptive capability in their design and operation.
This graduate certificate program provides practicing engineers the opportunity to develop the necessary skills in the use and development of computational intelligence algorithms based on evolutionary computation, neural networks, fuzzy logic, and complex systems theory. Engineers can also learn how to integrate common sense reasoning with computational intelligence elective courses such as data mining and knowledge discovery.

The certificate program consists of four courses, two core courses and two elective courses. In order to receive a graduate certificate, the student must have an average graduate cumulative grade point of 3.0 or better in the certificate courses taken.

Core Course:

Comp Eng 5310/ Elec Eng 5310/ Sys Eng 5211: Computational Intelligence

Select one course from the following:

Comp Sci 5400: Introduction to Artificial Intelligence
Comp Sci 5401: Evolutionary Computing
Sys Eng 5212/Elec Eng 5370: Introduction to Neural Networks and Applications

Elective Courses (select two courses not taken as a core course):

Comp Sci 5400: Introduction to Artificial Intelligence
Comp Sci 5401: Evolutionary Computing
Comp Eng 6330 / Elec Eng 6340 / Sys Eng 6214 / Stat 6239: Clustering Algorithms
Comp Sci 6400: Advanced Topics in Artificial Intelligence
Comp Sci 6401: Advanced Evolutionary Computing
Sys Eng 6215/ Comp Eng 6320 / Elec Eng 6360: Adaptive Dynamic Programming
Comp Sci 6402 / Sys Eng 6216 / Comp Eng 6302: Advanced Topics in Data Mining
Elec Eng 5320: Neural Networks for Control
Sys Eng 5212 / Elec Eng 5370: Introduction to Neural Networks and Applications
Mech Eng 6447 / Comp Eng 6310 / Eng Mgt 6410 / Aero Eng 6447 / Comp Sci 6202: Markov Decision Processes
Sys Eng 6213: Advanced Neural Networks

* Curriculum is subject to change. Please contact the department for up-to-date information on courses. Other courses approved by the department may be substituted for any of the above listed courses on a case-by-case basis. The administrative coordinators must approve the substitution prior to enrolling in the course.

Please check the Schedule of Classes for a current listing of the courses available for distance students.

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Comp Eng 5310/Sys Eng 5211: Computational Intelligence
Introduction to computational intelligence (CI), biological and artificial neuron, neural networks, evolutionary computing, swarm intelligence, artificial immune systems, fuzzy systems, & hybrid systems. CI application case studies covered include digital systems, control, power systems, forecasting and time-series predictions. Prerequisite: CS 1510 or programming competency. (Co-listed with Elec Eng 5310).

Sys Eng 5212 / Elec Eng 5370: Introduction to Neural Networks and Applications
Introduction to artificial neural network architectures, adaline, madaline, back propagation, BAM, and Hopfield memory, counterpropagation networks, self organising maps, adaptive resonance theory, are the topics covered. Students experiment with the use of artificial neural networks in engineering through semester projects. Prerequisites: Math 3304 or 3329; graduate standing.

Comp Sci 5400: Introduction to Artificial Intelligence
A modern introduction to AI, covering important topics of current interest such as search algorithms, heuristics, game trees, knowledge representation, reasoning, computational intelligence, and machine learning. Students will implement course concepts covering selected AI topics. Prerequisite: A 'C' or better grade in Comp Sci 2500.

Comp Sci 5401: Evolutionary Computing
Introduces evolutionary algorithms, a class of stochastic, population-based algorithms inspired by natural evolution theory (e.g., genetic algorithms), capable of solving complex problems for which other techniques fail. Students will implement course concepts, tackling science, engineering and/or business problems. Prerequisites: Comp Sci 2500 and a statistics course.

Elec Eng 5320: Neural Networks for Control
Introduction to artificial neural networks and various supervised and unsupervised learning techniques. Detailed analysis of some of the neural networks that are used in control and identification of dynamical systems. Application of neural networks in the area of control. Prerequisite: Elec Eng 3320.

Comp Sci 6400: Advanced Topics in Artificial Intelligence
Advanced topics of current interest in the field of artificial intelligence. This course involves reading seminal and state-of-the-art papers as well as conducting topical research projects including design, implementation, experimentation, analysis, and written and oral reporting components.  Prerequisite: Comp Sci 5400, Comp Sci 5401 or Comp Eng 5310.

Comp Sci 6401: Advanced Evolutionary Computing
Advanced topics in evolutionary algorithms, a class of stochastic, population-based algorithms inspired by natural evolution theory, capable of solving complex problems for which other techniques fail. Students will conduct challenging research projects involving advanced concept implementation, empirical studies, statistical analysis, and paper writing. Prerequisite: A 'C' or better grade in Comp Sci 5401.

Sys Eng 6213: Advanced Neural Networks
Advanced artificial neural network architectures, namely; radial-basis function networks, support vector machines, committee machines, principal components analysis, information-theoretic models, stochastic machines, neurodynamic programming, and temporal processing are the topics covered. Prerequisite: Sys Eng 5212 or equivalent neural network course.

Sys Eng 6214: Clustering Algorithms
An introduction to cluster analysis and clustering algorithms rooted in computational intelligence, computer science and statistics. Clustering in sequential data, massive data and high dimensional data. Students will be evaluated by individual or group research projects and research presentations. Prerequisite: At least one graduate course in statistics, data mining, algorithms, computational intelligence, or neural networks, consistent with student's degree program. (Co-listed with Comp Eng 6330, Elec Eng 6340, Comp Sci 6405 and Stat 6239)

  Sys Eng 6215/Comp Eng 6320/Elec Eng 6360: Adaptive Dynamic Programming
Review of neurocontrol and optimization, introduction to approximate dynamic programming (ADP), reinforcement learning (RL), combined concepts of ADP and RL - heuristic dynamic programming (HDP), dual heuristic programming (DHP), global dual heuristic programming (GDHP), and case studies. Prerequisite: Elec Eng 5370 Neural Networks or equivalent (Computational Intelligence Comp Eng 4001). (Co-listed with Comp Eng 6320, Mech Eng 6458, Aero Eng 6458, Elec Eng 6360 and Sys Eng 6215).

Comp Sci 6402/Sys Eng 6216/Comp Eng 6302: Advanced Topics in Data Mining
Advanced topics of current interest in the field of data mining. This course involves reading seminal and state-of-the-art papers as well as conducting topical research projects including design, implementation, experimentation, analysis, and written and oral reporting components. Prerequisite: A "C" or better grade in Comp Sci 5001 Introduction to Data Mining.

Mech Eng 6447/ Comp Eng 6310/ Eng Mgt 6410/ Aero Eng 6447/ Comp Sci 6202: Markov Decision Processes
Introduction to Markov decision processes & dynamic programming. Application to inventory control & other optimization & control topics. Prerequisiste: Graduate standing in background of probability or statistics.

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Admissions Requirements: Systems Engineering, Computer Science Departments

The only entrance requirement for students entering a graduate certificate program is that they satisfy the prerequisites for any course they take in the program.

Admissions Requirements: Department of Electrical and Computer Engineering 

  • BS degree in any field of engineering
  • A minimum of 24 months of post B.S professional work experience
  • GPA of 3.0 or better in the B.S degree
  • Average GPA of 3.0 or better (a grade of B or better) in the CT courses
  • 3 years to complete the CT
  • Employed while taking CT courses

Once admitted to the program, the student must take four designated courses as given above. In order to receive a graduate certificate, the student must have an average graduate grade point average of 3.0 or better in the certificate courses taken. Students admitted to the certificate program will have non-degree graduate status; however, if they complete the four-course sequence with a grade of B or better in each of the courses taken, they will be admitted to the MS program in electrical or computer engineering if they apply. The certificate courses taken by students admitted to the MS program will count towards their master's degrees. Students who do not have all of the prerequisite courses necessary to take the courses in the certificate program will be allowed to take "bridge" courses at either the graduate or undergraduate level to prepare for the formal certificate courses. Once admitted to the program, a student will be given three years to complete the program so long as he/she maintains a B average in the courses taken.

Click here for more information on admissions.

Students applying for a business project management graduate certificate will need the following:

Complete the Online Application. When you start your application select "Graduate Online/Distance" and then select which certificate you are applying for. Don't forget to list your current employer and company location on the application.

More Details

Gainful Employment Program Disclosure

Effective July 1, 2011, the Department of Education requires that all certificate programs must disclose particular Gainful Employment information to current and prospective students. The information that is provided in the disclosure includes the estimated cost of the certificate program as well as on-time graduation and job placement rates for this particular certificate program. The disclosure information is based on data from the 2015-16 school year (defined as the period between July 1, 2015, and June 30, 2016)

 

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Graduate certificates were designed as a gateway to a master’s degree. If a student earns a B or better in each certificate course they may continue for the graduate degree (in the corresponding department), without needing to submit GRE/GMAT scores, or letters of recommendation*. A student does not need to continue on for the graduate degree, however most do. Graduate certificates were designed for working professionals who have real life work experience and may not have time to take the GRE/GMAT. Admission requirements for the graduate certificate program are also more relaxed than the graduate degree. This graduate certificate may act as a gateway to the following master’s programs:

Computer Engineering (MS) 
Computer Science (MS)
Electrical Engineering (MS)
Systems Engineering (MS)

(Applicants must indicate which program they wish to use the certificate as a gateway for when initially applying for the certificate).

*Completion of a graduate certificate program does not automatically guarantee admission into a corresponding graduate degree program. To continue in a master’s degree program, you must apply. Click here for details and check with academic department for program specific details and requirements.

Classes are offered over the internet via live-streaming video. There will be a PowerPoint presentation available on the Internet to enhance the class lecture so that you can view the PowerPoint slides and watch the video simultaneously. Tests and reports are arranged by the course instructor. If you view the video online in real time, a technician can help resolve any communication problems, and when you need to ask a lecture-related question, you can call the instructor. If you view the lecture video offline, you can communicate with the instructor through email.

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FAQ

‌If you're like most prospective students, you have lots of questions about S&T's distance programs. We've tried to answer the most commonly asked questions, which can be found on the Frequently Asked Questions page. 

Hopefully, we've answered your questions. If we haven't, please send them our way. You can email us at global@mst.edu or call 573-341-4892 or toll free 1-877-678-1870.

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Contact Us

Dr. Sahra Sedighsarvestani
Associate Professor
Department of Electrical and
Computer Engineering
221 Emerson Hall
Rolla, MO 65409

Phone: 573-341-7505
Email: sedighs@mst.edu
Web: ece.mst.edu

Dr. Wei Jiang

Associate Chair for Graduate Studies & External Affairs
Associate Professor - Computer Science
333 Computer Science Bldg
500 W. 15th St.
Rolla, MO 65409-0350

Phone: 573-341-4989
Email: csgradcoord@mst.edu 
Web: cs.mst.edu

Dr. Cihan Dagli
Professor
Department of Systems Engineering &
Engineering Management

229 Engineering Management
Rolla, MO 65409

Phone: 573-647-9125
Email: dagli@mst.edu
Web: emse.mst.edu

Vicki Gibbons
Senior Assistant Director 
Student Support Services
Global Learning
216 Centennial Hall
Rolla, MO 65409-1560

Phone: 573-341-4892
Toll Free: 1-877-678-1870
Fax: 573-202-2396
Email: vgibbons@mst.edu