M.Eng. Special Topic Course Descriptions

Special Topic Courses offered in Winter 2024:

General Engineering Courses: 

ST: Advanced Entrepreneurship - ENGG*6090*01

This course provides students an opportunity to examine contemporary issues in entrepreneurship from an engineering perspective.  The course involves applying the technical, management, marketing, and financial aspects of entrepreneurship in order to integrate an engineering perspective to entrepreneurial initiatives. Topics relating to the wide spectrum of technological innovations will be examined from a business viewpoint. A casebased approach will be used to integrate both the engineering and business perspective in decision-making. Relevant topics including engineering innovations, social entrepreneurship, sustainability, and business model generation will be explored. Additional topics will vary according to the cases used.

ST: Communication in Engineering - ENGG*6090*02

Communication in Engineering empowers graduate students in engineering disciplines to communicate both within engineering disciplines and to broader audiences, such as the public, industry, and government. By recognizing genre, audience, and tools of meaning-making students develop a variety of skills for their academic and professional careers. The following genres are covered: peer-reviewed academic journal articles; research proposals; engineering poster and conference presentations; reports, and job applications. By writing original research projects directly related to their academic works (e.g., a thesis chapter, a journal article or conference presentation), students will improve their communication skills (written, oral, and visual) while achieving a tangible outcome or milestone in their graduate careers. Students create documents with established conventions and learn strategies to locate, analyze, summarize, cite, and critically engage the existing literature (through the ethical practice of database and other online research). Students are instructed on language, delivery, revision and peerreview to refine their own and peers’ documents (e.g., for precision, concision, clarity and specific issues of grammar, mechanics, or style).

ST: Computer Programming for Master of Engineering- ENGG*6090*03

A programming methodology course designed to familiarize the student with modern software development techniques. Emphasis is on problem-solving and structured program design methodologies. Programming projects are implemented in a widely used high-level language.

Biological Engineering Courses:

ST: Biologics and Biopharmaceuticals Process Design - ENGG*6190

This course deals with concepts of bioprocessing for biologics and biopharmaceutical process and product development. Topics include classes of biologics such as enzymes, hormones, antibodies, vaccines, DNA/RNA gene therapy products, cell and non-cell based systems for their synthesis, approaches to novel drug discovery, process design considerations, biomanufacturing technologies with a focus on quality-by-design (QbD) approach and high throughput and automated technologies for biologics discovery and product development, recovery and purification, ecopharmacovigilance to understand relation of biologics with environment including their fate and transport in the environment, and economic and regulatory concepts. Further understanding of bioprocessing of biologics will be provided through case studies of small RNA-based vaccines, and fate and impact of biologics on environment and ecosystem.

Prerequisite(s): This course is suitable for students who have an understanding of basic biochemistry or organic chemistry, thermodynamics, cell biology; and who are interested in learning how these concepts apply to biologics and biopharmaceuticals processing – from a biomanufacturing, pharmaceutical, biomedical/health, and environmental perspective.

Mechanical Engineering Courses: 

ST: Computer Aided Design - ENGG*6290*01

The course presents solid modeling of parts with increasing complexity and their assembly to form a final design. The simulation analysis of components and assemblies will be also covered in the course. The course also presents the underlying mathematics of geometric modeling of curves and surfaces.

ST: Design and Management of Energy Systems - ENGG*6290*02

There is a trend of energy facilities being outsourced by industry and building owners so that they can focus on their main areas of businesses. Professionals required to handle these energy facilities should have adequate skills to provide reliable energy services at competitive costs. A good understanding of the fundamental thermodynamic and engineering concepts, and techno-economic optimization techniques are essential for efficient designing and operation of such facilities. This course is intended to bridge thermal technology with systems engineering, and is application oriented.  A review of the fundamental concepts of energy and energy analysis of thermal processes is first demonstrated and then the students are exposed to the mathematical tools for characterization of the performance of energy equipments and optimization tools. Energy recovery by pinch technology will be discussed. Management of energy systems is also an important aspect to provide trouble free service to the industry.

ST: Advanced Engineering Materials - ENGG*6290*03

Advanced Engineering Materials (AEMs) encompass a broad spectrum of engineering study, research and application. AEMs are studied in diverse fields of engineering, including Mechanical, Biomedical, Biological, Environmental, and beyond (Aerospace, Chemical, Civil, Electrical, Materials, Mechatronics, Metallurgy, Nanotechnology, etc.). Research on AEMs involves understanding chemical and physical properties of novel materials at the molecular to macroscopic scales, finding ways to synthesize these novel materials and scale-up their production, applying various analytical and instrumental techniques to characterize and test them, and assessing their suitability as a replacement for traditional materials or in new applications that traditional materials are unsuitable for. Finally, AEMs find applications that range from conventional ones, such as building materials, water treatment, and electronics, to innovative ones such as electrical vehicles, high efficiency solar panels and wind turbines, carbon dioxide adsorbents, and artificial tissues. In this course, various examples of AEMs will be looked at from the points of view mentioned above (synthesis/manufacturing, properties/characterization, and testing/applications). These aspects of AEMs will be discussed via delivered lectures and a series of weekly student seminars. Students will also be guided on the use of bibliographic searchers, and bibliometric and scientometric analyses for identifying AEMs and understanding their historical and technological development. The goal of this course is to impart students with ENGG*6290 03 F23 v1.00 the ability to critically and deeply think about AEMs, so that they can also more critically and deeply think about their research, including the role or use of AEMs in their field of research.

ST: Advanced Manufacturing - ENGG*6290*04

This course focuses on different advanced topics in manufacturing. Among the topics covered will be: What is Advanced Manufacturing?, Theory of Metal Machining, Computer Numerical Control (CNC), Additive Manufacturing and 3D Printing Technology Review, Laser Machining/ Welding, and Composite Materials, Automation in Manufacturing. These topics will be covered through a combination of lectures, student presentations, and projects.

ST: HVAC & Building Energy Management - ENGG*6290*05

The HVAC course provides fundamentals for engineers to practice HVAC design. The course prepare students to compute heating and cooling load for buildings. Students will be able to select HVAC equipment and perform duct design for buildings. Course will address the energy management techniques for HVAC systems in buildings. Students will be required to design HVAC system for a selected building as part of the course project.

 ST: Advanced Manufacturing of Hot Stamped Automotive Parts - ENGG*6290*06

Course description to follow once approved

 

Engineering Systems and Computing / Computer Engineering:

ST: Advanced Machine Learning - ENGG*6600*01

Machine Learning is a subfield of Artificial Intelligence that focuses on studying techniques and algorithms that enable computer systems to learn from experience. This course serves as a foundation for further academic or industry work in the age of big data. This course places special emphasis on the area of data preparation (preprocessing, postprocessing), algorithm comparison and evaluation, complexity analysis of algorithm. The course covers both supervised and unsupervised learning algorithms along with advanced ensemble based machine learning techniques. It also seeks to clarify and  explain the relationship between traditional machine learning and deep learning. Students are encouraged to explore practical applications of these techniques across a wide variety of engineering domains.

ST: Advanced Topics in Signals Processing - ENGG*6600*02

This course is a course on signal processing techniques. As an example, biomedical signals of the human and body, and analysis of these signals will be discussed, and the concepts and methodology can be applied for processing other types of signals. The main goals of the course are (1) to teach students the fundamental physiological processes of the human body and how biomedical signals are generated, (2) to illustrate the proper instrumentation setup for signal collection and (3) to illustrate clearly the way signals may be processed using Matlab and other software packages.

ST: Privacy-Preserving Artificial Intelligence Systems - ENGG*6600*03

Course description to follow

 

ST: Advanced Embedded System Design - ENGG*6600*04

This course introduces the basic principles of embedded system design. It utilizes advanced hardware/software abstractions and languages to help design complex systems. Topics include:

• Hardware language for design and simulation. System-on-chip designs and integration using processor cores and dedicated core modules. •]

• On-chip busses and bus protocols.

• Hardware/software interfaces.

• Co-processor design.

• Embedded computing platforms.

• Cryptography basics, security for embedded systems.

• Design examples. 

• Current research topics in embedded systems design

ST: Cyber Security for Engineers - ENGG*6600*05

Today’s engineers are dealing with sophisticated critical infrastructures with increasing dependency on the Internet of Things (IoT) and communication networks, which turns them into highly complex Cyber-Physical Systems (CPSs). Although integrating Information Technology (IT) in Operational Technologies (OT) improves efficiency, reliability, and sustainability, it leads to more and more security vulnerabilities. This course serves as a graduate introduction to cybersecurity and discusses different attack detection, penetration testing, and digital forensics techniques, and offers an in-depth review of technological solutions in each domain.

ST: Deep Learning - ENGG*6600*06

This course focuses on various topics in deep learning, covering topics from fundamental concepts in neural networks to state-of-the-art deep learning. We start with one neuron (Perceptron, ADALINE, logistic regression) and one-layer networks (radial basis function, selforganizing map). Then, we cover feed-forward nets, backpropagation, stochastic gradient descent, AdaGrad, RMSProp, and Adam. Then, convolutional neural nets and important CNN architectures are introduced. Regularization techniques such as weight decay, batch normalization, and dropout are also explained. Then, we cover sequence modeling (useful for NLP and speech processing) including RNN, LSTM, attention, transformers, BERT, and GPT. Deep metric learning and Siamese network are introduced for data embedding. Next, generative models are covered including variational models, generative moment matching nets, GAN, and diffusion models. Depending on the time, the other covered topics can be Boltzmann machines, graph neural nets, knowledge distillation for network compression, deep reinforcement learning, meta-learning, federated learning, explainable AI, self-supervised learning, and the theory of optimization in networks. Some applications of deep learning, including usage in computer vision, image processing, and NLP, are also introduced.

ST: Image Analysis - ENGG*6600*07

The course covers numerous topics in image analysis (image enhancement, segmentation, registration, classification, machine learning, …) with specific examples on applications to medical images (e.g. brain tumor detection, cardiac functional imaging, and image-guided surgery). It is intended for graduate students from various backgrounds who wish to acquire basic knowledge in image processing.

ST: Information Retrieval  - ENGG*6600*08

This course covers information retrieval and other information processing activities, from an applied perspective. There will be numerous programming projects and assignments. Topics will include: search engine construction (document acquisition, processing, indexing, and querying); learning to rank; information retrieval system performance evaluation; classification and clustering; other machine learning information processing tasks (e.g., basic deep learning models for information retrieval); and many more. 

ST: Internet of Things - ENGG*6600*09

The course introduces you to the design and implementation of an IoT system. You will develop an app from scratch, assuming a basic knowledge of Java, and learn how to set up Android Studio, work with various Activities and create simple user interfaces to make your apps run smoothly. This course serves as a foundation for further academic or industry work in Smart Cities and Internet of Things

 

ST: Statistical Machine Learning - ENGG*6600*10

This course focuses on statistical machine learning, which is almost most of the machine learning without a deep learning approach. We start with the preliminaries, the mapping model, and tasks in machine learning. Then, overfitting, cross validation, and regularization are introduced. Classic classification methods, such as LDA, QDA, SVM, kernel SVM, Bayes, KNN, trees, and random forest are covered. Regression models, including linear regression, ridge regression, and lasso regression, are introduced. Bagging and boosting (AdaBoost) are then explained. Afterwards, mixture distributions and Gaussian mixture models are covered. Then, spectral and probabilistic feature extraction, including PCA, FDA, MDS, Isomap, LLE, variational inference, factor analysis, probabilistic PCA, t-SNE, UMAP, and metric learning, are explained. In the meantime, we cover point estimation including MLE and EM algorithms. If time allows, other topics can be covered including probabilistic graphical models (Markov models, factor graphs, HMM, and MCMC), causal inference, clustering algorithms, and outlier (anomaly) detection

 

ENV-WRE

ST: Tracers in Hydrogeology - ENGG*6790*01

The course focuses on the use of tracers as a tool to identify and quantify processes across the critical zone, from precipitation to the water table, and in groundwater. The course covers the fundamentals of subsurface flow and transport in porous media and fractured aquifers, focusing on unsaturated zone flow and groundwater recharge assessment. In addition, the course discusses the global issues associated with groundwater use, the link between climate change and groundwater and strategies for the protection and sustainable use of groundwater resources.

ST: Colloid/Interface Science and Applications - ENGG*6790*02

This course focuses on the theory and applications of colloid and interface science in diverse sectors, relevant to engineers, physicists and chemists. Major topics include the forces of interactions between colloids, the stabilization and destabilization of emulsions and foams, and polymeric fluids and gels.

ST: Environmental Fluid Mechanics - ENGG*6790*03

Environmental Fluid Mechanics (EFM) is a graduate course designed for future scientists and engineers to develop the fundamental and applied knowledge needed to understand, analyze, and design flow processes that occur in the environment: e.g. flows for the atmosphere, oceans, rivers, built environment, and engineering applications. Physical and mathematical properties of fluid flow are investigated. These include equations of motion, statistical description of turbulent flows, mean flow equations, wall flows, scales of turbulent motion, and time and frequency domains. Practical aspects of measuring and analyzing fluid flow are also investigated. These include fundamentals of measurement, in-situ techniques, sonic and ultrasonic techniques, and electromagnetic techniques.