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Artificial Intelligence, Intelligent Systems, Machine Learning, Natural Language Processing

Our Researchers

See Texas A&M University researchers in the areas of artificial intelligence, intelligent systems, machine learning, and natural language processing.

Theodora Chaspari

  • Assistant Professor, Computer Science & Engineering
Theodora Chaspari

Yoonsuck Choe

  • Professor, Computer Science & Engineering
Yoonsuck Choe

Ricardo Gutierrez-Osuna

  • Professor, Computer Science & Engineering
Ricardo Gutierrez-Osuna

Tracy Anne Hammond

  • Professor, Computer Science & Engineering
  • Director, Institute for Engineering Education and Innovation
Tracy Anne Hammond

Ruihong Huang

  • Associate Professor, Computer Science & Engineering
Ruihong Huang

Thomas R. Ioerger

  • Professor, Computer Science & Engineering
Thomas R. Ioerger

Shuiwang Ji

  • Professor, Computer Science & Engineering
Shuiwang Ji

Andruid Kerne

  • Professor, Computer Science & Engineering
Andruid Kerne

Abdullah Muzahid

  • Assistant Professor, Computer Science & Engineering
Abdullah Muzahid

Guni Sharon

  • Assistant Professor, Computer Science & Engineering
Guni Sharon

Dylan Shell

  • Associate Professor, Computer Science & Engineering
Dylan Shell

Dezhen Song

  • Professor, Computer Science & Engineering
  • Associate Department Head for Academics, Computer Science & Engineering
Dezhen Song

Courtesy Appointments

 

Nick Duffield

  • Professor, Electrical & Computer Engineering
  • Royce E. Wisenbaker Professor I
  • Director, Texas A&M Institute of Data Science
Nick Duffield

Courses Offered

CSCE 625. Artificial Intelligence. Credits 3. 3 Lecture Hours

Basic concepts and methods of artificial intelligence; Heuristic search procedures for general graphs; game playing strategies; resolution and rule based deduction systems; knowledge representation; reasoning with uncertainty.

Prerequisite: CSCE 221.

CSCE 631. Intelligent Agents Parallel Algorithm Design and Analysis. Credits 3. 3 Lecture Hours

On the design and implementation of Intelligent Agents and coordination mechanisms among multiple agents, ranging from theoretical principles to practical methods for implementation. 

Prerequisite: CSCE 420 or CSCE 625.

CSCE 627. Theory of Computability. Credits 3. 3 Lecture Hours

Formal models of computation such as pushdown automata; Turing machines and recursive functions; unsolvability results; complexity of solvable results. 

Prerequisite: CSCE 433.

CSCE 636. Neural Networks. Credits 3. 3 Lecture Hours

Basic concepts in neural computing; functional equivalence and convergence properties of neural network models; associative memory models; associative, competitive and adaptive resonance models of adaptation and learning; selective applications of neural networks to vision, speech, motor control and planning; neural network modeling environments. 

Prerequisite: MATH 304 and MATH 308 or approval of instructor.

CSCE 639/MEEN 676. Fuzzy Logic and Intelligent Systems. Credits 3. 3 Lecture Hours

Introduces the basics of fuzzy logic and its role in developing intelligent systems; topics include fuzzy set theory, fuzzy rule inference, fuzzy logic in control, fuzzy pattern recognition, neural fuzzy systems and fuzzy model identification using genetic algorithms.  

Prerequisite: CSCE 625 or approval of instructor. 
Cross Listing: MEEN 676/CSCE 639.

CSCE 644. Cortical Networks. Credits 3. 3 Lecture Hours

The architecture of the mammalian cerebral cortex; its modular organization and its network for distributed and parallel processing; cortical networks in perception and memory; neuronal microstructure and dynamical simulation of cortical networks; the cortical network as a proven paradigm for the design of cognitive machines. 

Prerequisite: CSCE 420 or CSCE 625 and CSCE 636 and graduate classification.