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Course List

Upcoming Courses (Fall 2023)

  • CS 501: Mathematical Foundations of Computer Science (Course Site)
    • The mathematics you need for computer science and computation
  • CS 520: Graduate Introduction to AI (Course Site)
    • A broad introduction to topics in artificial intelligence, only one of which is ML!

Past Courses

  • CS 205: Intro to Discrete Math and Logic (Course Site)
    • The purpose of this course is to give a groundwork in thinking mathematically with an emphasis on logical construction and analysis, and the mathematical objects you’ll need to be familiar with throughout computer science.
  • CS 440: Introduction to AI (Undergraduate) (Course Site)
    • This course serves as a broad introduction to artificial intelligence, focusing primarily on the problem of representation – how to represent problems of interest in ways that computers can begin to process them ‘intelligently’, and the algorithms necessary to do so. Runs the gamut from knowledge representation, to search algorithms, to machine learning. The undergraduate version parallels the graduate version, but at an appropriate level and pace.
  • CS 501: Mathematical Foundations of Computer Science (Course Site)
    • This course serves as an introduction or refresher to mathematical topics necessary in computer science. These are topics that you will need to be familiar with and comfortable with to be successful in areas like data science, machine learning, and artificial intelligence, to name a few. Topics include linear algebra, probability and statistics, and optimization methods.
  • CS 520: Introduction to AI (Graduate) (Course Site)
    • This course serves as a broad introduction to artificial intelligence, focusing primarily on the problem of representation – how to represent problems of interest in ways that computers can begin to process them ‘intelligently’, and the algorithms necessary to do so. Runs the gamut from knowledge representation, to search algorithms, to machine learning. This is the graduate level version of this course.
  • CS 536: Introduction to Machine Learning (Graduate) (Course Site)
    • This course serves as an introduction to topics in machine learning. Particular emphasis is given to understanding data from a probabilistic perspective, and designing and training models to describe and analyze that data. On the practical side, we discuss and implement algorithms for things like decision trees, SVMs, and neural networks; on the theoretical side, we try to understand the mathematical basis for these models and their training algorithms, and their limitations (over fitting, under fitting, and what it means to learn, mathematically).
  • Special Topic: Introduction to R (Course Site)
    • This was a short special topics course offered to MS students. Introducing the R programming language/environment was used as an excuse to explore various statistics and data science ideas.
  • CS 674: Mathematics of Artificial Intelligence (Archival) (Course Site)
    • This was one of my first official courses taught at Rutgers – some material is dated, some material has gotten rolled into some of the other courses listed above. But enough of it stands on its own. The emphasis here was mathematical approaches in artificial intelligence – specifically in areas like optimization (algorithms for finding the best model or solution in a given context), statistical inference, and online learning algorithms.