Autonomy and Robotics Faculty and Courses

Autonomy and Robotics Faculty

See Texas A&M University autonomy and robotics engineering instructors.

Artificial Intelligence/Machine Learning Courses

AERO 626 Estimation of Dynamic Systems. Credits 3. 3 Lecture Hours

Traditional concepts and recent advances in estimation related to modern dynamic systems found in aerospace disciplines; least squares estimation, state estimation, nonlinear filtering, aircraft position and velocity tracking, attitude determination of spacecraft vehicles, gyro bias estimation and calibration.

Prerequisite: AERO 310 or equivalent STAT 211 or equivalent.

AERO 630 Introduction to Random Dynamical Systems. Credits 3. 3 Lecture Hours

Building on basic probability theory, course covers theory and applications of discrete and continuous random processes. Particular attention shall be paid to the response of dynamical systems (discrete, linear and non-linear), to random input processes and their application to Engineering Systems.

Prerequisite: Graduate classification

AERO 631 Model Predictive Control for Aerospace Systems. Credits 3. 3 Lecture Hours

Nonlinear optimal control and optimization, optimal control theory, dynamical systems stability and control, approximation theory, convex optimization; control of engineering systems with state and control constraints with parametric uncertainty; formulate optimal control problems, solve as nonlinear programming problems using available solvers; requires background in control theory.

Prerequisite: Graduate classification and AERO 623 or comparable course.

AERO 632 Design of Advanced Flight Control Systems - Theory and Application. Credits 3. 3 Lecture Hours

Modeling, analysis, design and implementation of advanced flight control problems, specifically aerospace engineering applications; includes choice of controlled variables, reduction of controlled variables, design methodology, computational framework, implementation issues, and software environments using various toolboxes.

Prerequisite: Graduate classification and approval of instructor.

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.

ECEN 629 Applied Convex Optimization. Credits 3. 3 Lecture Hours

Introduction to convex optimization including convex set, convex functions, convex optimization problems, KKT conditions and duality, unconstrained optimization, and interior-point methods for constrained optimization; applications in information science, digital systems, networks and learning.

Prerequisite: ECEN 601 or equivalent.

ECEN 647 Information Theory. Credits 3. 3 Lecture Hours

Definition of information; coding of information for transmission over a noisy channel including additive Gaussian noise channels and waveform channels; minimum rates at which sources can be encoded; maximum rates at which information can be transmitted over noisy channels..

Prerequisite: ECEN 646 or equivalent.

ECEN 649 Pattern Recognition. Credits 3. 3 Lecture Hours

Introduction to the underlying principles of classification, and computer recognition of imagery and robotic applications.

Prerequisite: MATH 601 and/or STAT 601 and approval of instructor.

ECEN 662 Estimation and Detection Theory. Credits 3. 3 Lecture Hours

Probabilistic signal detection theory and parameter estimation theory; Neyman-Pearson, UMP, and locally optimal tests; discrete time Markov processes and the Kalman and Wiener filters; bayesian, maximum likelihood and conditional mean estimation methods.

Prerequisite: ECEN 646 or equivalent.

ECEN 748 Data Stream Algorithms and Applications. Credits 3. 3 Lecture Hours

Study of algorithms to sample, sketch and summarize high rate data streams, including applications to measuring internet traffic and services and transactional graph streaming data; quantify the trade-offs between computational and storage resources and accuracy that are inherent in these methods.

Prerequisite: Graduate classification: ECEN 303 or previous undergraduate or graduate course in probability or statistics; or approval of instructor.

ECEN 758 Data Mining and Analysis. Credits 3. 3 Lecture Hours

Broad overview of data mining, integrating related concepts from machine learning and statistics; exploratory data analysis, pattern mining, clustering and classification; applications to scientific and online data.

ECEN 760 Introduction to Probabilistic Graphical Models. Credits 3. 3 Lecture Hours

Broad overview of various probabilistic graphical models, including Bayesian networks, Markov networks, conditional random fields, and factor graphs; relevant inference and learning algorithms, as well as their application in various science and engineering problems will be introduced throughout the course. Prerequisites: Undergraduate level probability theory; basic programming skill in any programming language (C, C++, Python, Matlab, etc.).

ECEN 765 Machine Learning with Networks. Credits 3. 3 Lecture Hours

Scientific analysis of large-scale data; introduction to advanced methods that are designed to analyze structured data represented as networks.