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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
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):
3 units from Mathematics 277, Applied Mathematics 219 or Mathematics 331; and 3 units from Engineering 233, Digital Engineering 233 or Digital Engineering 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.
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
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 units; (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 units; (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 units; (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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COURSES OF INSTRUCTION