Graduate Certificate - Computational Intelligence

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.

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.

For prerequisites regarding the courses below, please visits the Catalog of Courses provided by the Registrar's Office.

Core Courses

Sys Eng 5211/ Comp Eng 5310/ Elec Eng 5310 Computational Intelligence
Introduction to Computational Intelligence (CI), Biological and Artificial Neuron, Neural Networks, Evolutionary Computing, Swarm Intelligence, Artificial Immune Systems, Fuzzy Systems, and Hybrid Systems. CI application, case studies covered include digital systems, control, power systems, forecasting, and time-series predictions. Prerequisites: Comp Sci 1510 or programming competency.

-select one of the following-

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, counter propagation networks, self organizing maps, adaptive resonance theory, are the topics covered. Students experiment with the use of artificial neural networks in engineering through semester projects.  Prerequisites: graduate standing.

Comp Sci 5401 Evolutionary Computing
Introduces evolutionary algorithms, a class of stochastic, population-based algorithms inspired by natural evolution (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. 

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. Prerequisites: Comp Sci 2500.

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

Comp Sci 5400  Introduction to Artificial Intelligence
(Course description is provided above)

Comp Sci 5401 Evolutionary Computing
(Course description is provided above)

Sys Eng 5212/ Elec Eng 5370 Introduction to Neural Networks and Applications
(Course description is provided above)

Comp Eng 6330/ Elec Eng 6340/ Sys Eng 6214/ Stat 6239  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. Prerequisites: At least one graduate course in statistics, data mining, algorithms, computational intelligence, neural networks, consistent with student's degree program. 

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 or 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: Comp Sci 5401. 

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), Duel Heuristic Programming (DHP), Global Dual Heuristic Programming (GDHP) and case studies. Prerequisite: Sys Eng 5212/Elec Eng 5370 or Comp Eng 5310. 

Sys Eng 6216/ Comp Sci 6402/ 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.  

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. Applications of neural networks in the area of Control. Case studies and a term project. Prerequisite: Elec Eng 3320. 

Comp Eng 6310/ Mech Eng 6447/ Aero Eng 6447/ Eng Mgt 6410/ Comp Sci 6202 Markov Decision Processes
Introduction to Markov Decision Processes and Dynamic Programming. Application to Inventory Control and other optimization and control topics.
Co-listed: AE 6447, CpE 6310, CS 6202, ME 6447

Sys Eng 6213 Deep Learning and 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, Temporal Processing are the topics covered. Prerequisite: Sys Eng 5212 or equivalent neural network course.  

Admission Requirements
This certificate program is open to all persons holding a BS, MS, or PhD degree with a degree in Engineering or hard science with a minimum cumulative GPA of 2.75 and who have a minimum of 12 months of professional employment experience or are currently accepted into a graduate degree program at Missouri S&T.

Once admitted to the program, the student must take the four designated courses. In order to receive a Graduate Certificate, the student must have an cumulative grade point average of 3.0 or better in the certificate courses. Once admitted into the program, a student will be given three years to complete the program.

Students admitted to the Certificate Program will have non-degree graduate status but will earn graduate credit for the courses they complete. If the four-course sequence is completed with a grade of "B" or better in each of the courses, they will be admitted to the appropriate MS program if they apply. The Certificate course taken by students admitted to the MS program will count towards their master’s degree. 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.