Listed below are the undergraduate level AI courses. The tentative CS graduate level course schedule for the next term is posted on the CS current course offerings page, in which 600-level courses are open to undergraduate students as 400-level courses. For a complete list of undergraduate level CS courses, please visit the CS course information page.
Introduction to modeling and algorithmic techniques for machines to learn concepts from data. Generalization: underfitting, overfitting, cross-validation. Tasks: classification, regression, clustering. Optimization-based learning: loss minimization, regularization. Statistical learning: maximum likelihood, Bayesian learning. Algorithms: nearest neighbor, (generalized) linear regression, mixtures of Gaussians, Gaussian processes, kernel methods, support vector machines, deep learning, sequence learning, ensemble techniques. Large scale learning: distributed learning and stream learning. Applications: Natural language processing, computer vision, data mining, human computer interaction, information retrieval.
Introduction to image and vision understanding by computer. Camera-system geometry, image formation and lighting, and image acquisition. Basic visual processes for recognition of edges, regions, lines, and surfaces. Processing of stereo images, and motion in image sequences. Object recognition. Applications of computer vision systems.
Extracting meaningful patterns from random samples of large data sets. Statistical analysis of the resulting problems. Common algorithm paradigms for such tasks. Central concepts: VC-dimension, Margins of classifier, Sparsity and description length. Performance guarantees: Generalization bounds, data dependent error bounds and computational complexity of learning algorithms. Common paradigms: Neural networks, Kernel methods and Support Vector machines, Applications to Data Mining.
Goals and methods of artificial intelligence. Methods of general problem solving. Introduction to mathematical logic Mechanical theorem proving. Game playing. Natural language processing. Preference will be given to CS graduate students. All others require approvalfrom the department. Department approval will be by Undergraduate Advisor.
This number is used for courses being offered on a temporary basis. Such a course may be available only once, for example to take advantage of a visiting professor's expertise, or may be offered experimentally until it is determined whether of not the course should become part of the regular course offerings. It may also be used for an individual study course carried out under the supervision of a Computer Science faculty member with the approval from the Associate Chair, Graduate Studies. This is a grade course. Preference will be given to CS graduate students. All others require approval of the Department.
- Neural Networks (J. Orchard, Winter 2020)
- Artificial Intelligence: Law, Ethics, and Policy (M. Grossman, Fall, 2019)
- Computational Audio (R. Mann, Winter 2019)
- Neural Networks (J. Orchard, Winter 2019)
- Computational Audio (R. Mann, Winter 2018)
- Machine Learning (P. Poupart, Winter 2018)
- Neural Networks (J. Orchard, Winter 2018)
- Machine Learning (Y. Yu, Fall 2017)
- Computational Audio (R. Mann, Winter 2017)
- Machine Learning (P. Poupart, Winter 2017)
- Rhetoric, Argument and Machines (C. Di Marco, Winter 2017)
- Computational Audio (R. Mann, Winter 2016)