Undergraduate Courses

CSE 415: Introduction To Artificial Intelligence Principles and programming techniques of artificial intelligence: Python, symbol manipulation, knowledge representation, logical and probabilistic reasoning, learning, language understanding, vision, expert systems, and social issues. Intended for non-majors. Not open for credit to students who have completed CSE 473. Prerequisite: CSE 373.
CSE 427: Computational Biology Algorithmic and analytic techniques underlying analysis of large-scale biological data sets such as DNA, RNA, and protein sequences or structures, expression and proteomic profiling. Hands-on experience with databases, analysis tools, and genome markers. Applications such as sequence alignment, BLAST, phylogenetics, and Markov models. Prerequisite: CSE 312; CSE 332.
CSE 428: Computational Biology Capstone Designs and implements a software tool or software analysis for an important problem in computational molecular biology. Prerequisite: CSE 312; CSE 331; CSE 332.
CSE 446: Machine Learning Design of efficient algorithms that learn from data. Representative topics include supervised learning, unsupervised learning, regression and classification, deep learning, kernel methods, and optimization. Emphasis on algorithmic principles and how to use these tools in practice. Prerequisite: CSE 332; MATH 208 or MATH 136; and either STAT 390, STAT 391, or CSE 312.
CSE 455: Computer Vision Introduction to image analysis and interpreting the 3D world from image data. Topics may include segmentation, motion estimation, image mosaics, 3D-shape reconstruction, object recognition, and image retrieval. Prerequisite: CSE 333; CSE 332; recommended: MATH 208; STAT 391.
CSE 473: Introduction To Artificial Intelligence Principal ideas and developments in artificial intelligence: Problem solving and search, game playing, knowledge representation and reasoning, uncertainty, machine learning, natural language processing. Not open for credit to students who have completed CSE 415. Prerequisite: CSE 312; CSE 332.
CSE 481c: Robotics Capstone Students work in teams to design and implement algorithms for robotic perception and control.

Professional (Evening) Courses

CSEP 573: Applications Of Artificial Intelligence Introduction to the use of Artificial Intelligence tools and techniques in industrial and company settings. Topics include foundations (search, knowledge representation) and tools such as expert systems, natural language interfaces and machine learning techniques. Prerequisite: CSE majors only.

Graduate Courses

CSE 525: Randomized Algorithms And Probablisitc Analysis Examines algorithmic techniques: random selection, random sampling, backwards analysis, algebraic methods, Monte Carlo methods, and randomized rounding; random graphs; the probabilistic method; Markov chains and random walks; and analysis tools: random variables, moments and deviations, Chernoff bounds, martingales, and balls in bins. Prerequisite: CSE 521 or equivalent; CSE majors only. Offered: WSp.
CSE 528: Computational Neuroscience Introduction to computational methods for understanding nervous systems and the principles governing their operation. Topics include representation of information by spiking neurons, information processing in neural circuits, and algorithms for adaptation and learning. Prerequisite: elementary calculus, linear algebra, and statistics, or by permission of instructor. Offered: jointly with NEUBEH 528.
CSE 546: Machine Learning Explores methods for designing systems that learn from data and improve with experience. Supervised learning and predictive modeling; decision trees, rule induction, nearest neighbors, Bayesian methods, neural networks, support vector machines, and model ensembles. Unsupervised learning and clustering. Prerequisite: either STAT 341, STAT 391, or equivalent, or permission of instructor.
CSE 571: Probabilistic Robotics This course introduces various techniques for Bayesian state estimation and its application to problems such as robot localization, mapping, and manipulation. The course will also provide a problem-oriented introduction to relevant machine learning and computer vision techniques.
CSE 573: Artificial Intelligence Broad introduction to the science of automated rational decision-making by machines. Key approaches include search, Markov decision processes, graphical models, reinforcement learning, and supervised learning. Considers a wide variety of application domains (e.g., natural language processing, computer vision, robotics, games) for decision making.
CSE 574: Explainable Artificial Intelligence Approaches to enhancing the interpretability and transparency of complex machine learning models, encompassing both inherently interpretable models and post hoc explanation methods. Explores a spectrum of techniques, ranging from feature attributions and their evaluation metrics to counterfactual explanations, concept-based explanations, instance explanations, and collaboration between humans and artificial intelligence.
CSE 576: Computer Vision Overview of computer vision, emphasizing the middle ground between image processing and artificial intelligence. Image formation, preattentive image processing, boundary and region representations, and case studies of vision architectures. Prerequisite: Solid knowledge of linear algebra, good programming skills, CSE or E E major or permission of instructor. Offered: jointly with E E 576.
CSE 577: Special Topics In Computer Vision Topics vary and may include vision for graphics, probabilistic vision and learning, medical imaging, content-based image and video retrieval, robot vision, or 3D object recognition. Prerequisite: CSE/E E 576 or permission of instructor. Offered: jointly with E E 577.