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CNS 155: Probabilistic Graphical Models
9.00 Credits
California Institute of Technology
Many real-world problems in AI, computer vision, robotics, computer systems, computational neuroscience, computational biology, and natural language processing require one to reason about highly uncertain, structured data, and draw global insight from local observations. Probabilistic graphical models allow addressing these challenges in a unified framework. These models generalize approaches such as hidden Markov models and Kalman filters, factor analysis, and Markov random fields. In this course, we will study the problem of learning such models from data, performing inference (both exact and approximate), and using these models for making decisions. The techniques draw from statistics, algorithms, and discrete and convex optimization. The course will be heavily research- oriented, covering current developments such as probabilistic relational models, models for naturally combining logical and probabilistic inference, and nonparametric Bayesian methods. Not offered 2012–13.
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CNS 155 - Probabilistic Graphical Models
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CNS 156 ab: Learning Systems
9.00 Credits
California Institute of Technology
Introduction to the theory, algorithms, and applications of automated learning. How much information is needed to learn a task, how much computation is involved, and how it can be accomplished. Special emphasis will be given to unifying the different approaches to the subject coming from statistics, function approximation, optimization, pattern recognition, and neural networks. Part a Instructor: Abu-Mostafa; Part b Not offered 2012–13.
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CNS 156 ab - Learning Systems
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CNS 157: Comparative Nervous Systems
9.00 Credits
California Institute of Technology
An introduction to the comparative study of the gross and microscopic structure of nervous systems. Emphasis on the vertebrate nervous system; also, the highly developed central nervous systems found in arthropods and cephalopods. Variation in nervous system structure with function and with behavioral and ecological specializations and the evolution of the vertebrate brain. Instructor: Allman. Given in alternate years; offered 2012–13.
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CNS 157 - Comparative Nervous Systems
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CNS 158: Vertebrate Evolution
9.00 Credits
California Institute of Technology
An integrative approach to the study of vertebrate evolution combining comparative anatomical, behavioral, embryological, genetic, paleontological, and physiological findings. Special emphasis will be given to: (1) the modification of developmental programs in evolution; (2) homeostatic systems for temperature regulation; (3) changes in the life cycle governing longevity and death; (4) the evolution of brain and behavior. Given in alternate years; not offered 2012–13.
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CNS 158 - Vertebrate Evolution
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CNS 159: Projects in Machine Learning and AI
9.00 Credits
California Institute of Technology
Students are expected to execute a substantial project in AI and/or machine learning, write up a report describing their work, and make a presentation. Not offered 2012–13.
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CNS 159 - Projects in Machine Learning and AI
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CNS 162: Cellular and Systems Neuroscience Laboratory
12.00 Credits
California Institute of Technology
A laboratory-based introduction to experimental methods used for electrophysiological studies of the central nervous system. Through the term, students investigate the physiological response properties of neurons in insect and mammalian brains, using extra- and intracellular recording techniques. Students are instructed in all aspects of experimental procedures, including proper surgical techniques, electrode fabrication, stimulus presentation, and computer-based data analysis. Graded pass/fail. Instructor: Staff.
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CNS 162 - Cellular and Systems Neuroscience Laboratory
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CNS 171: Introduction to Computer Graphics Laboratory
12.00 Credits
California Institute of Technology
This course introduces the basic ideas behind computer graphics and its fundamental algorithms. Topics include graphics input and output, the graphics pipeline, sampling and image manipulation, three-dimensional transformations and interactive modeling, basics of physically based modeling and animation, simple shading models and their hardware implementation, and fundamental algorithms of scientific visualization. Students will be required to perform significant implementations. Instructor: Barr.
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CNS 171 - Introduction to Computer Graphics Laboratory
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CNS 174: Computer Graphics Projects
12.00 Credits
California Institute of Technology
This laboratory class offers students an opportunity for independent work covering recent computer graphics research. In coordination with the instructor, students select a computer graphics modeling, rendering, interaction, or related algorithm and implement it. Students are required to present their work in class and discuss the results of their implementation and any possible improvements to the basic methods. May be repeated for credit with instructor’s permission. Instructor: Barr.
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CNS 174 - Computer Graphics Projects
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CNS 176: Cognition
12.00 Credits
California Institute of Technology
The cornerstone of current progress in understanding the mind, the brain, and the relationship between the two is the study of human and animal cognition. This course will provide an in-depth survey and analysis of behavioral observations, theoretical accounts, computational models, patient data, electrophysiological studies, and brain-imaging results on mental capacities such as attention, memory, emotion, object representation, language, and cognitive development. Instructor: Shimojo; offered 2012–13.
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CNS 176 - Cognition
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CNS 180: Research in Computation and Neural Systems
1.00 - 9.00 Credits
California Institute of Technology
No course description available.
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CNS 180 - Research in Computation and Neural Systems
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