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

SysEng 5211 (367) 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.
Co-listed: CpE 5310, EE 5310

-select one of the following-

SysEng 5212 (378) 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.
Co-listed: EE 5370

CS 5401 (348) 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.

CS 5400 (347) 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. 

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

CS 5400 (347) Introduction to Artificial Intelligence
(Course description is provided above)

CS 5401 (348) Evolutionary Computing
(Course description is provided above)

SysEng 5212 (378) Introduction to Neural Networks and Applications
(Course description is provided above)

Sys Eng 6214 (439) 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: CpE 6330, EE 6340, Stat 6239

CS 6400 (447) 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.

CS 6401 (448) 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.

SysEng 6215 (458) Adaptive Critic Designs

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.
Co-listed: CpE 6320, EE 6360

SysEng 6216 (404) Data Mining and Knowledge Discovery

Data mining and knowledge discovery utilizes both classical and new algorithms, such as machine learning and neural networks, to discover previously unknown relationships in data. Key data mining issues to be addressed include knowledge representation and knowledge acquisition (automated learning).
Co-listed: CpE 6302

EE 5320 (337) 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.

EngMgt 6410 (457) 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

SysEng 6213 (478) 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. 

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.

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 2010-11 school year (defined as the period between July 1, 2010, and June 30, 2011).