Description

Principles of digital image processing including image enhancement and multispectral classification. Emphasis upon design and implementation of remote sensing systems and analysis of remotely sensed data for geotechnical and environmental investigations

Learning Objective

1. Understand the fundamental principles of remote sensing technology and its relevance to geological engineering.
2. Identify and select appropriate remote sensing data sources for geological and geotechnical applications.
3. Apply remote sensing techniques to geological hazard assessment, land use planning, and environmental monitoring.
4. Interpret remote sensing data to extract geological and geotechnical information.
5. Integrate remote sensing data with other geological engineering data sources for comprehensive analysis.
6. Demonstrate proficiency in using remote sensing software and tools.
7. Communicate findings effectively through reports and presentations.

Course Content

• Remote Sensing Data Sources
• Data Preprocessing and Enhancement
• Image Interpretation and Classification
• Remote Sensing Applications in Geological Engineering

Course Evaluation Criteria

• Term Project
• Midterm Exam
• Final Exam

Description

This course surveys seminal scholarship in the field of international security and explores its relevance to contemporary geopolitical issues. Specific topics addressed may include space security, nuclear security, and technological change in military affairs.

Learning Objective

1. Understand the foundational principles of international security studies.
2. Analyze contemporary geopolitical issues from a security perspective.
3. Assess the significance of space security in the modern global landscape.
4. Evaluate the challenges and implications of nuclear security.
5. Examine the impact of technological advancements on military affairs.

Course Content

• Historical Perspectives on Security
• Technological Change in Military Affairs
• Security Strategies and Alliances
• Security in Regional Contexts
• Nuclear Security

Course Evaluation Criteria

• Midterm Exam
• Assignments
• Final Paper

Description

Applications of Windows-based Visual Basic solutions to engineering problems including selected topics in fluid flow, PVT behavior, matrices in engineering solutions, translating curves to computer solutions, predictor-corrector material balance solutions, and graphical display of results.

Learning Objective

1. Utilize Windows-based Visual Basic programming to solve complex petroleum engineering problems.
2. Apply digital tools to analyze fluid flow behavior. 
3. Understand the behavior of hydrocarbons under different pressure and temperature conditions (PVT analysis).
4. Manipulate matrices for solving engineering problems related to reservoirs and well performance.
5. Translate mathematical curves into computer-based solutions for engineering analysis.
6. Implement predictor-corrector methods for material balance calculations.
7. Create graphical representations of engineering results for effective data visualization.
8. Analyze real-world petroleum engineering problems and develop software solutions.

Course Content

• Fluid Flow Analysis
• Matrices in Engineering Solutions
• Translating Curves to Computer Solutions
• Future Trends and Emerging Technologies

Course Evaluation Criteria

• Assignments
• Course Project
• Final exam

Description

Statistical methods in engineering and geological applications including site investigations and environmental data analyses. Introduction to spatial correlation analysis and geostatistical techniques such as kriging for resource evaluation and estimation.

If you enroll in this course, you cannot enroll in Advanced Statistical Methods in Geology and Engineering (GEO ENG 5315) for this certificate.

Learning Objective

1. To understand the importance of statistical methods in geological engineering.
2. To acquire proficiency in collecting and organizing geological and engineering data.
3. To learn and apply various statistical techniques for data analysis.
4. To develop critical thinking and problem-solving skills for geological engineering challenges.
5. To communicate and present statistical findings effectively.

Course Content

• Data Collection and Sampling
• Probability and Probability Distributions
• Exploratory Data Analysis
• Hypothesis Testing
• Spatial Correlation Analysis 
• Resource Evaluation and Estimation

Course Evaluation Criteria

• Assignments
• Term Project
• Final Exam\

Description

Application of statistical methods to study geologic materials and practices, with emphasis on reliable interpretation of laboratory and field data for water, hydrocarbon, and mineral exploration, research, and engineering as well as other aspects of geological engineering. 

If you enroll in this course, you cannot enroll in Statistical Methods in Geology and Engineering (GEO ENG 4115) for this certificate.

Learning Objective

1. Mastering Fundamental Statistical Techniques
2. Application of Statistical Tools in Geological Investigations
3. Quality Control and Assurance in Geological Data
4. Data-Driven Decision Making
5. Effective Communication of Statistical Results

Course Content

• Fundamentals of Statistical Analysis in Geological Engineering
• Design of Experiments in Geological Engineering
• Spatial Analysis and Geo-statistics in Geological Engineering
• Reliability and Risk Analysis in Geological Engineering
• Advanced Topics in Geological Engineering Statistics

Course Evaluation Criteria

• Assignments
• Term Project
• Midterm Exam
• Final Exam

Description

Applications of Geographical Information Systems and remote sensing to environmental monitoring, mineral resource exploration, and geotechnical site evaluation.

Learning Objectives

1. Understand the core concepts and principles of Geographic Information Systems (GIS).
2. Acquire and manage geospatial data for geological engineering applications.
3. Perform geospatial analysis and modeling to solve geological problems.
4. Create effective maps and visualizations for geological presentations and reports.
5. Apply GIS techniques to real-world geological engineering projects.
6. Evaluate the ethical and environmental considerations in GIS applications.

Course Content

• Geospatial Data Acquisition and Management
• Spatial Analysis Techniques
• GIS in Geological Exploration
• Environmental Impact Assessment
• GIS Project Implementation

Course Evaluation Criteria

• Assignments
• Term Project
• Midterm Exam
• Final Exam

Description

Scientific programming in a UNIX/Linux environment, with emphasis on solving geophysical problems such as linear and nonlinear inversion, spectral analysis, seismicity, seismic wave attenuation, shear-wave splitting, and seismic tomography.

Learning Objective

1. To provide students with a strong foundation in scientific programming within a UNIX/Linux environment.
2. To enable students to apply computational techniques to solve geophysical problems.
3. To familiarize students with key geophysical concepts and methodologies.
4. To develop students' ability to analyze and interpret geophysical data using programming skills.

Course Content

• Linear Inversion Techniques
• Nonlinear Inversion and Optimization
• Spectral Analysis in Geophysics
• Seismicity and Seismic Wave Attenuation
• Shear-Wave Splitting Analysis
• Seismic Tomography

Course Evaluation Criteria

• Assignments
• Course Project
• Final Exam

Description

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.

If you enroll in this course, you cannot enroll in Introduction to Data Mining (COMP SCI 5402) for this certificate.

Learning Objective

1. Provide students with foundational knowledge of artificial intelligence (AI) principles and concepts.
2. Familiarize students with real-world AI applications across various domains.
3. Equip students with the ability to apply AI techniques to solve problems and make informed decisions.

Course Content

• Key AI concepts: machine learning, neural networks, and natural language processing.
• Ethical considerations in AI.
• AI Techniques
• Neural networks and deep learning basics.
• Supervised and unsupervised learning.
• AI Applications
• Chatbots and virtual assistants.
• AI in image and speech recognition.
• Hands-on exercises in developing and training AI models.
• Evaluation and interpretation of AI results.
• Introduction to popular AI libraries (e.g., TensorFlow, PyTorch).
• Practical demonstrations and exercises using AI software.
• How AI is transforming various industries.
• Career opportunities in AI-related fields.
• Ethical considerations and AI's impact on society.

Course Evaluation Criteria

• Project 
• Midterm Exam 
• Final Exam

Description

The key objectives of this course are two-fold: (1) to teach the fundamental concepts of data mining and (2) to provide extensive hands-on experience in applying the concepts to real-world applications. The core topics to be covered in this course include classification, clustering, association analysis, data preprocessing, and outlier/novelty detection.

If you enroll in this course, you cannot enroll in Introduction to Artificial Intelligence (COMP SCI 5400) for this certificate.

Learning Objective

1. Fundamental Knowledge: Provide students with a basic understanding of data mining principles and techniques.
2. Practical Skills: Equip students with the ability to apply data mining methods to extract valuable insights from large datasets.
3. Real-World Applications: Illustrate how data mining is used in various fields and industries for decision-making and problem-solving.

Course Content

• Data Mining Basics: Introduction to key concepts, tasks, and preprocessing.
• Techniques: Overview of popular algorithms, hands-on implementation, and evaluation.
• Applications: Real-world examples and ethical considerations.
• Tools: Introduction to data mining software and practical usage.
• Process: Understanding the data mining lifecycle.
• Data Visualization: Importance and techniques for data visualization.
• Big Data: Challenges and opportunities in mining large datasets.
• Projects: Hands-on application of data mining techniques.

Course Evaluation Criteria

• Project 
• Midterm Exam 
• Final Exam

Description

This course will familiarize geologists, geophysicists, civil and geological engineers with the fundamental principles of physical geology, geohydrology and geomorphology as applied to military problems, such as the development of fortifications, core infrastructure, water resources, and combat engineering requirements.

Learning Objective

1. Apply geological principles to military decision-making processes.
2. Analyze terrain and geological conditions to assess their impact on military operations.
3. Evaluate geological resources for military engineering and logistical purposes.
4. Identify and mitigate geohazards in military planning and infrastructure development.
5. Understand the role of geospatial technology in military geology.

Course Content

• Geological Fundamentals for Military Geology
• Terrain Analysis and Military Operations
• Geospatial Technology in Military Geology
• Geological Resources for Military Operations
• Military Infrastructure and Environmental Impact

Course Evaluation Criteria

• Assignments
• Term Project
• Final Exam

Description

Quantitative methods of utilizing remote sensing technology for terrain analysis. Digital image processing of landsat and/or aircraft scanner data for mineral resource studies and geological engineering applications.

Learning Objective

1. Understand the principles of remote sensing and its applications in geological engineering.
2. Demonstrate proficiency in acquiring and processing remote sensing data.
3. Apply advanced image processing methods to extract geological information.
4. Interpret and analyze remote sensing data for geological mapping, hazard assessment, and resource exploration.
5. Develop practical solutions to real-world geological engineering problems using remote sensing techniques.

Course Content

• Remote Sensing Data Sources
• Image Interpretation and Analysis
• Remote Sensing for Geological Mapping
• Remote Sensing for Resource Exploration
• Advanced Image Processing Techniques

Course Evaluation Criteria

• Assignments
• Midterm Exam
• Term Project

Description

This course introduces the technology used for both surface and subsurface geologic mapping. It utilizes common systems and programs such as UNIX, Windows, and industry-standard mapping applications. The goal of the course is to fully prepare students for their first professional assignment.

Learning Objective

1. To provide students with a solid foundation in computational techniques used in geology.
2. To develop proficiency in utilizing the UNIX operating system for geologic data analysis.
3. To familiarize students with industry-standard mapping applications and software tools.
4. To enhance students' geospatial skills for surface and subsurface geologic mapping.
5. To prepare students for their first professional assignment in the field of geology by providing practical experience with real-world geologic datasets.

Course Content

• Geographic Information Systems (GIS) and Remote Sensing in Geology
• 3D Modeling and Visualization
• Field Data Acquisition and Processing
• Industry-standard Mapping Applications
• Geospatial Analysis

Course Evaluation Criteria

• Assignments
• Midterm Exam
• Term Project

Description

Introduces techniques of modern machine learning methods with applications in marketing, finance, and other business disciplines. Topics include regression, classification, resampling methods, model selection, regularization, decision trees, support vector machines, principal component analysis, and clustering. R programming is required. Prerequisites: knowledge of calculus, statistics, and programming.

Learning Objective

1. To develop a solid understanding of the foundational concepts and techniques in modern machine learning.
2. To gain practical proficiency in applying machine learning methods to real-world business scenarios, including applications in marketing, finance, and other business disciplines.
3. To become proficient in the selection of appropriate models and techniques for data analysis.
4. To develop the ability to implement and utilize various machine learning algorithms, including decision trees, support vector machines, principal component analysis, and clustering, to solve business-related problems.
5. To acquire advanced programming skills in R to effectively implement and experiment with machine learning algorithms.

Course Content

• Regression Analysis and Predictive Modeling
• Classification Algorithms for Business Applications
• Feature Selection and Model Selection Strategies
• Support Vector Machines in Business Analytics
• Principal Component Analysis for Dimensionality Reduction
• Applications of Machine Learning in Marketing and Finance

Course Evaluation Criteria

• HWs
• Project
• Final Exam

Description

Use of deep learning and advance neural networks in the design of cyber physical complex adaptive systems. Machine learning basics, deep feed forward networks, regularization for deep learning, optimization for training deep models, convolutional networks, recurrent and recursive nets, practical, vision and natural language processing applications.

Learning Objective

1. Understand the fundamental concepts of deep learning and advanced neural networks.
2. Apply various deep learning architectures to solve complex engineering problems.
3. Analyze and interpret the results of deep learning models in the context of systems engineering.
4. Implement state-of-the-art deep learning techniques for pattern recognition, image analysis, and natural language processing.
5. Evaluate the ethical and societal implications of advanced neural networks in engineering systems.
6. Collaborate effectively on projects that involve deep learning solutions.

Course Content

• Introduction to Deep Learning
• Feedforward Neural Networks
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Natural Language Processing (NLP)
• Deep Learning in Systems Engineering

Course Evaluation Criteria

• Assignments
• Final Exam 
• Final Project