Find here a listing of some courses recommended by current and former BiSSL students:
Mechanical Engineering Courses (MEEN)
MEEN 602 Modeling and Analysis of Mechanical Systems Essentially linear algebra for mechanical engineers. Satisfies one MATH requirement. Recommended to anyone in our lab, suggest that you take this in the first semester before taking MEEN 683 – will allow a better understanding of the optimization mathematics.
MEEN 683 Multidisciplinary Systems Analysis and Design Optimization
Very relevant to BiSSL research, would recommend to anyone in our lab. The course is completely project-based and students are suggested to use their own research for the project and write the project report in terms of a conference paper – a great place to get your first conference paper.
MEEN 440/696 Bio-inspired Engineering Design
Very useful and relevant course. In terms of research work, the course will help in developing your state of the art description, as well as learning tools to analyze your problem and the bio-inspired solution. I would recommend to anyone in BiSSL.
MEEN 601 Advanced Product Design
Design methodology, functional design, innovation, parameter analysis, design for reliability, manufacturability and strength; design project.
MEEN 489/697 (Spring) – Developing an Innovation Mindset
Cultivate your creativity and apply techniques to increase innovation in your design, research, and business activities. Studio sessions are opportunities to apply concepts and receive personal coaching. Class concepts will be applied to team design/research projects for increased impact.
MEEN 669 Alternative Energy Conversion
Useful as an early course if interested in current alternative energy technology practice and availability (technical as well as fiscal). Useful if renewable energy technology is related to one’s thesis.
MEEN 648/ISEN 654 Manufacturing Systems Planning and Analysis
The system perspective of a computer integrated manufacturing system; manufacturing and its various levels and the planning and control of product movement through the production system in the context of using realtime control, multiprocessor systems, network architectures and databases.
MEEN 653 Scientific Writing
Topics include origin and development of scientific writing, research methods, outlines, paper organization, journal selection, strategies to build a productive personal writing culture, effective communication, critical reviews and submission; preparation of an original manuscript for submission to a peer-reviewed journal by the end of the semester.
MEEN 489/689 Machine Learning for Mechanical Engineers
Machine learning is the study of self-modifying computer systems that can acquire new knowledge and improve their own performance; survey machine learning techniques, which include induction from examples, conceptual clustering, explanation-based learning, exemplar learning and analogy, discovery and genetic algorithms.
A&M Courses Outside of Mechanical Engineering
ESSM 671 Ecological Economics
Good for understanding terms to use from ecology and economics.
WFSC 689 (Spring) Ecological Applications in R
Teaches statistical methods used in Ecological Analysis and performing those analyses in R. A good place to get hands-on R learning.
ISEN 660 Quantitative Risk Analysis
This course is very well structured and relevant for those interested in risk analysis. There is a strong emphasis on uncertainty analysis which is crucial in complex systems engineering and risk analysis. Very detailed and methodical. The focus is more on reliability engineering.
STAT 630 Overview of Mathematical Statistics
Basic probability theory including distributions of random variables and expectations. Introduction to the theory of statistical inference from the likelihood point of view including maximum likelihood estimation, confidence intervals, and likelihood ratio tests. Introduction to Bayesian methods.
STAT 639 Data Mining and Analysis
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.
ISEN 671 Human Error and Resilient System Design Variables that influence human performance including cognitive, behavioral and physical factors are examined and discussed; case studies from a variety of industries and domains are employed to illustrate the identification and causes of human errors; system design strategies for reducing the likelihood of error occurrence and mitigating error consequences are practically applied to real design cases from industry, military and government agencies.
ISEN 613 Engineering Data Analysis
Selected topics in probability and data analysis for quality in engineering problems; measurement principles, data collection and data analysis to solve quality engineering problems. Introduction to courses in the assurance sciences-reliability, maintainability, quality control and robust design.
ISEN 622 Linear Programming
Development of the mathematics and algorithms associated with linear programming; convex sets and cones, polyhedral sets, duality theory, sensitivity analysis, simplex, revised simplex and dual simplex methods; also covered are bounded variables, column generation, decomposition, integer programming; computer assignment.
LDEV 671 Sustainable Development
Sustainability perspectives about values, rights, property and what constitutes an optimum human environment; sustainability principles and case studies emphasizing on-the ground, incentive-based land development that balances economic growth with environmental quality.
CVEN 642/BAEN 642 Water-Energy-Food Nexus: Toward a Sustainable Resource Management
Principles and application of the Water-Energy-Food nexus to state, national and international Water-Energy-Food securities and the inter-linkages between them; exploration of quantitative framework to develop and assess sustainable trade offs of resources; hands on experiences; relevant real world projects or case studies.
WFSC 604 Ecological Modeling
(Spring course) Weekly modeling exercises help students develop & utilize a simulation model to address a specific question in ecology, natural resource management, or a related field. Semester projects are presented both written and orally
CVEN 466 Sustainability and Life Cycle Assessment
(Spring/Summer course) Definitions of sustainability and sustainable development from social, economic, political, and technical perspectives; life-cycle analysis and quantitative assessment of sustainability; industrial ecology; valuation of environmental goods and externalities; sustainable infrastructure design
AERO 689 Foundations of Systems Engineering
Selected topics in probability and data analysis for quality in theoretical foundations behind systems engineering. Including introductions to discrete mathematics (set theory, logic, formal languages, graph theory), probability theory and random processes, optimization (linear, non-linear, discrete, dynamic, multi-objective), and if time permits some special topics (utility theory, information theory).
Alternative Learning Opportunities
TAMIDS Data Science Webinars
Texas A&M Institute of Data Science invites you to attend the “TAMIDS Data Science Webinars” from Apr 1 to Apr 15, 2020. This webinar series is to introduce the fundamentals of data science (with python) to students and researchers from the Texas A&M University system.
Texas A&M Institute of Data Science Workshop
“Reinforcement Learning: Algorithms and Applications” by Dr. Dileep Kalathil, will be held Friday, April 3, 2020. Reinforcement Learning (RL) is extremely useful, starting from classical stochastic control problems to applications in robotics, drones, games, healthcare and self-driving cars. Tutorial will cover the fundamental theory and concepts, state-of-the-art algorithms, and successful applications of reinforcement
National Science Foundation Summer School Programs
“Decision Making in Engineering Systems” at the University of Southern California, Los Angeles, June 23-29, 2018. The six-day summer school introduced graduate students to the foundations of decision making in large systems.