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ENGR183/EMGT289: Fundamentals of Statistical Quality Engineering
ENGR183/EMGT289: Fundamentals of Statistical Quality Engineering
Level: Undergraduate & Graduate
Level: Undergraduate & Graduate
This course focuses on the definition and applications of Six-Sigma quality systems for design production, engineering applications, and business processes. The main topics include statistical methods in quality control and assurance, implementation strategies, practical engineering applications for achieving continuous quality improvement, defect reduction, and quality-related project planning and management methods to achieve universal participation in process improvement.
This course focuses on the definition and applications of Six-Sigma quality systems for design production, engineering applications, and business processes. The main topics include statistical methods in quality control and assurance, implementation strategies, practical engineering applications for achieving continuous quality improvement, defect reduction, and quality-related project planning and management methods to achieve universal participation in process improvement.
ECEN520: Introduction to Machine Learning
ECEN520: Introduction to Machine Learning
Level: Graduate
Level: Graduate
This graduate-level course in Machine Learning provides an in-depth exploration of both theoretical and practical aspects of statistical learning. The course covers a range of topics, including supervised learning techniques such as linear regression, logistic regression, and support vector machines, as well as unsupervised learning methods like clustering and dimensionality reduction. Students will also work on machine learning concepts such as ensemble methods, neural networks, and model evaluation techniques. A strong emphasis is placed on hands-on problem-solving, with projects and assignments designed to reinforce the theoretical concepts using Python. Throughout the course, students will gain practical experience in applying machine learning algorithms to real-world datasets, developing skills in data preprocessing, feature selection, model tuning, and performance evaluation.
This graduate-level course in Machine Learning provides an in-depth exploration of both theoretical and practical aspects of statistical learning. The course covers a range of topics, including supervised learning techniques such as linear regression, logistic regression, and support vector machines, as well as unsupervised learning methods like clustering and dimensionality reduction. Students will also work on machine learning concepts such as ensemble methods, neural networks, and model evaluation techniques. A strong emphasis is placed on hands-on problem-solving, with projects and assignments designed to reinforce the theoretical concepts using Python. Throughout the course, students will gain practical experience in applying machine learning algorithms to real-world datasets, developing skills in data preprocessing, feature selection, model tuning, and performance evaluation.
ENGR187/EMGT287: Applications of AI in Manufacturing
ENGR187/EMGT287: Applications of AI in Manufacturing
Level: Undergraduate & Graduate
Level: Undergraduate & Graduate
This course explores the application of artificial intelligence in the manufacturing ecosystem, focusing on optimizing production processes through AI technologies. Participants will learn how to predict production rates, analyze machinery performance, and plan preventive maintenance using data-driven AI methods. The syllabus covers essential AI tools and frameworks, including Python, TensorFlow, and Keras, along with foundational topics like machine learning concepts, neural networks, and deep learning techniques. Practical sessions include case studies on manufacturing applications, enabling students to apply AI in real-world scenarios. Key areas such as CNN architectures, sequence processing, and regularization techniques will be thoroughly examined, culminating in comprehensive case studies and project-based learning.
This course explores the application of artificial intelligence in the manufacturing ecosystem, focusing on optimizing production processes through AI technologies. Participants will learn how to predict production rates, analyze machinery performance, and plan preventive maintenance using data-driven AI methods. The syllabus covers essential AI tools and frameworks, including Python, TensorFlow, and Keras, along with foundational topics like machine learning concepts, neural networks, and deep learning techniques. Practical sessions include case studies on manufacturing applications, enabling students to apply AI in real-world scenarios. Key areas such as CNN architectures, sequence processing, and regularization techniques will be thoroughly examined, culminating in comprehensive case studies and project-based learning.
ENGR1: Introduction to Engineering
ENGR1: Introduction to Engineering
Level: Undergraduate
Level: Undergraduate
This course provides an introduction to engineering, including fundamentals of engineering study, different engineering disciplines, and interdisciplinary aspects of engineering. This course investigates the connection between science, technology and society and also illustrates the extent to which engineering impacts the world. The course also exposes students to entrepreneurship, engineering professionalism, the growth mindset, emerging markets, ethics, and civic engagement. ENGR 1 and ENGR 1L together fulfill the Science, Technology & Society core requirement.
This course provides an introduction to engineering, including fundamentals of engineering study, different engineering disciplines, and interdisciplinary aspects of engineering. This course investigates the connection between science, technology and society and also illustrates the extent to which engineering impacts the world. The course also exposes students to entrepreneurship, engineering professionalism, the growth mindset, emerging markets, ethics, and civic engagement. ENGR 1 and ENGR 1L together fulfill the Science, Technology & Society core requirement.
ENGR1L: Introduction to Engineering Lab
ENGR1L: Introduction to Engineering Lab
Level: Undergraduate
Level: Undergraduate
The laboratory will provide students with hands-on experience of engineering design and open-ended problem solving. The lab focuses on introducing aspects of the different engineering disciplines and allows students to gain experience with each of the engineering disciplines and reflect on learning gains with teamwork, communication, and engineering skills. Engineering designs will be framed to include the impact of design solutions/technologies on society and will be developed in a team-based environment utilizing visuals, written text, and oral presentation. ENGR 1 and ENGR 1L together fulfill the Science, Technology & Society core requirement.
The laboratory will provide students with hands-on experience of engineering design and open-ended problem solving. The lab focuses on introducing aspects of the different engineering disciplines and allows students to gain experience with each of the engineering disciplines and reflect on learning gains with teamwork, communication, and engineering skills. Engineering designs will be framed to include the impact of design solutions/technologies on society and will be developed in a team-based environment utilizing visuals, written text, and oral presentation. ENGR 1 and ENGR 1L together fulfill the Science, Technology & Society core requirement.
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