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For more information about these courses, see the .
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Digital Engineering
233
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Programming with Data
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Fundamental programming constructs and data structures. Algorithm development and problem solving. Programming techniques to facilitate data analysis. Obtaining and cleaning data. Data validation. Data manipulation. Data visualization. Introduction to decision making using machine learning. Applications chosen from all engineering disciplines.
Course Hours:
3 units; (3-2)
Antirequisite(s):
Credit for Digital Engineering 233 and any of Computer Science 217, 231, 235, Computer Engineering 339 or Engineering 233 will not be allowed.
Also known as:
(formerly Engineering 233)
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Digital Engineering
310
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Fundamentals of Software Design and Development
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Introduction to design and implementation of software systems for engineering applications. Software design lifecycle. Source code management systems. Debugging and testing techniques. Illustration of common data structures and fundamental algorithms using a high level language. Libraries for input/output. Software tools to facilitate data analysis and visualization.
Course Hours:
3 units; (3-2)
Prerequisite(s):
Engineering 233 or Digital Engineering 233; and admission to the Digital Engineering Minor.
Antirequisite(s):
Credit for Digital Engineering 310 and Engineering 519.06 (Digital Engineering I) will not be allowed.
Also known as:
(formerly Software Engineering for Engineers 310)
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Digital Engineering
319
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Probability, Statistics and Machine Learning
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Presentation and description of data, introduction to probability theory, Bayes' theorem, discrete and continuous probability distributions, estimation, sampling distributions, tests of hypotheses on means, variances and proportions; Introduction to fundamental machine learning including linear regression, classification and correlation. Applications are chosen from engineering practice from all disciplines. Course Hours: Prerequisite(s): Antirequisite(s): Also known as:
Course Hours:
3 units; (3-1.5T)
Prerequisite(s):
Mathematics 277 or 331; and one of Engineering 233, Digital Engineering 233 or 440.
Antirequisite(s):
Credit for Digital Engineering 319 and Biomedical Engineering 319 will not be allowed.
Also known as:
(formerly Engineering 319)
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Digital Engineering
407
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Computational Numerical Methods
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Numerical computational procedures to solve engineering problems. Introduction to computational libraries that support matrix operations. Developing and implementing programs for: solution of linear and non-linear equations, curve fitting, solution of the algebraic eigenvalue problems, interpolation, differentiation, integration and solution of differential equations. The course will include the programming projects that address comprehensive engineering problems. Algorithm development and application labs.
Course Hours:
3 units; (3-2T)
Prerequisite(s):
Engineering 233 or Digital Engineering 233; and Mathematics 375.
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Antirequisite(s):
Credit for Digital Engineering 407 and Chemical Engineering 407 will not be allowed.
Also known as:
(formerly Engineering 407)
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Digital Engineering
410
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Fundamentals of Applied Artificial Intelligence
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Techniques for extracting, cleaning, and visualizing data from engineering applications. Basic numerical computation techniques underlying learning algorithms. Fundamental supervised and unsupervised learning algorithms. Emphasis will be on designing practical applications that leverage existing software libraries and frameworks to solve problems in various engineering disciplines.
Course Hours:
3 units; (3-2)
Prerequisite(s):
3 units from Software Engineering for Engineers 310, 337, Digital Engineering 310, Computer Engineering 335 or Geomatics Engineering 333; and admission to the Digital or Aerospace Engineering Minor.
Antirequisite(s):
Credit for Digital Engineering 410 and either Software Engineering for Engineers 519.47 (Applied Data Science) or 544 will not be allowed.
Also known as:
(formerly Software Engineering for Engineers 410)
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Digital Engineering
411
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Advanced Applied Artificial Intelligence and Machine Learning
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Advanced supervised and unsupervised learning algorithms. Introduction to modern neural network architectures. Emphasis will be on designing practical applications that leverage existing software libraries and frameworks to solve engineering problems.
Course Hours:
3 units; (3-2)
Prerequisite(s):
Software Engineering for Engineers 410 or Digital Engineering 410; and admission to the Digital Engineering Minor.
Antirequisite(s):
Credit for Digital Engineering 411 and either Software Engineering for Engineers 519.47 (Applied Data Science) or Electrical Engineering 525 will not be allowed
Also known as:
(formerly Software Engineering for Engineers 411)
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Digital Engineering
440
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Introduction to Python
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Programming concepts using Python, including statements, conditionals, loops, functions, file I/O, debugging, data parsing and display, and use of libraries.
Course Hours:
1 unit; (1-1)
Prerequisite(s):
Admission to the BSc Energy Engineering program.
Antirequisite(s):
Credit for Digital Engineering 440 and either Engineering 233 or Digital Engineering 233 will not be allowed.
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Digital Engineering
450
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Digital Security
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Topics in information security including cryptography, encryption, hashes, block chain and cryptocurrency. Security issues in Cyberphysical systems. Human factors including social engineering, malware, phishing and computer viruses.
Course Hours:
1 unit; (1-1)
Prerequisite(s):
Engineering 233 or Digital Engineering 233.
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Digital Engineering
451
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Industrial Internet of Things
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Topics in Internet of Things and Industrial Internet of Things, including sensors, embedded computers, networking and connectivity, cloud and data storage.
Course Hours:
1 unit; (1-1)
Prerequisite(s):
Engineering 233 or Digital Engineering 233.
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Digital Engineering
452
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Augmented and Virtual Reality
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Topics in Augmented and Virtual Reality including AR/VR hardware, basic programming of AR/VR applications, deployment of 3D CAD models in AR/VR.
Course Hours:
1 unit; (1-1)
Prerequisite(s):
Engineering 233 or Digital Engineering 233.
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Digital Engineering
453
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Advanced Software Programming
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Object-oriented design. Concurrency issues in programs. Socket programming. GUI design and event-driven programming.
Course Hours:
1 unit; (1-1)
Prerequisite(s):
Engineering 233 or Digital Engineering 233.
Antirequisite(s):
Not open to students in the BSc Software Engineering program or the Digital Engineering Minor.
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Digital Engineering
454
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Fundamentals of Web Development
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Programming techniques and tools for developing web applications. Basics of frontend and database design. Web server fundamentals.
Course Hours:
1 unit; (1-1)
Prerequisite(s):
Digital Engineering 453.
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Digital Engineering
455
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Advanced Data Engineering
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Advanced tools and techniques to facilitate exploratory data analysis. Web scraping and parsing. Libraries to facilitate rapid data analysis. Data visualization techniques.
Course Hours:
1 unit; (1-1)
Prerequisite(s):
Engineering 233 or Digital Engineering 233.
Antirequisite(s):
Not open to students in the BSc Software Engineering program or the Digital Engineering Minor.
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Digital Engineering
456
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Intermediate Machine Learning for Engineers
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Feature engineering techniques. Linear and non-linear regression and classification techniques. Basic unsupervised learning algorithms.
Course Hours:
1 unit; (1-1)
Prerequisite(s):
3 units from Engineering 319, Digital Engineering 319 or Electrical Engineering 419; and Digital Engineering 455.
Antirequisite(s):
Not open to students in the BSc Software Engineering program or the Digital Engineering Minor.
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Digital Engineering
457
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Advanced Machine Learning for Engineers
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Deep learning, reinforcement learning, and advanced unsupervised learning algorithms.
Course Hours:
1 unit; (1-1)
Prerequisite(s):
Digital Engineering 456.
Antirequisite(s):
Not open to students in the BSc Software Engineering program or the Digital Engineering Minor.
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Digital Engineering
510
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Cyber-Physical Systems Engineering
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Fundamental principles related to the design of cyber-physical systems. Techniques to ensure reliability, performance, and security of cyber-physical systems. Emphasis will be on developing practical applications from engineering domains.
Course Hours:
3 units; (3-2)
Prerequisite(s):
Software Engineering for Engineers 311 or Digital Engineering 311; and admission to the Digital Engineering Minor.
Antirequisite(s):
Credit for Digital Engineering 510 and Computer Engineering 511 will not be allowed
Also known as:
(formerly Software Engineering for Engineers 510)
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Digital Engineering
511
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Industrial Internet of Things Systems and Data Analytics
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Fundamentals of Industrial Internet of Things (IIoT) Systems. Digital and software frameworks to support IIoT data analytics.
Course Hours:
3 units; (3-2)
Prerequisite(s):
Software Engineering for Engineers 510 or Digital Engineering 510; and admission to the Digital Engineering Minor.
Also known as:
(formerly Software Engineering for Engineers 511)
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COURSES OF INSTRUCTION