16.410 Principles of Autonomy and Decision Making (MIT)
This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization
9.01 Neuroscience and Behavior (MIT)
This course covers the relation of structure and function at various levels of neuronal integration. Topics include functional neuroanatomy and neurophysiology, sensory and motor systems, centrally programmed behavior, sensory systems, sleep and dreaming, motivation and reward, emotional displays of various types, "higher functions" and the neocortex, and neural processes in learning and memory.
15.818 Pricing (MIT)
This course, primarily discussion based, provides a framework for understanding pricing strategies and tactics. Topics covered include pricing in competitive markets, estimating demand, price discrimination, the role of price cues, anticipating competitive responses, pricing in business to business markets, and pricing of new products. Lectures and cases are the primary modes of learning.
2.160 Identification, Estimation, and Learning (MIT)
This course provides a broad theoretical basis for system identification, estimation, and learning. Students will study least squares estimation and its convergence properties, Kalman filters, noise dynamics and system representation, function approximation theory, neural nets, radial basis functions, wavelets, Volterra expansions, informative data sets, persistent excitation, asymptotic variance, central limit theorems, model structure selection, system order estimate, maximum likelihood, unbia
9.520 Statistical Learning Theory and Applications (MIT)
This course is for upper-level graduate students who are planning careers in computational neuroscience. This course focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. It develops basic tools such as Regularization including Support Vector Machines for regression and classification. It derives generalization bounds using both stability and VC theory. It also dis
21F.104 Chinese IV (Regular) (MIT)
This is the last of the four courses (Chinese I through IV) that make up the foundation level (four semesters over two years in the normal curriculum) of MIT's regular (non-streamlined) Chinese program. Chinese IV is designed to consolidate conversational usage and grammatical and cultural knowledge encountered in the earlier courses, and to expand reading and listening abilities. It integrates the last part of Learning Chinese (two units designed primarily for review of grammatical concepts and
11.965 Reflective Practice: An Approach for Expanding Your Learning Frontiers (MIT)
The course is an introduction to the approach of Reflective Practice developed by Donald Schön. It is an approach that enables professionals to understand how they use their knowledge in practical situations and how they can combine practice and learning in a more effective way. Through greater awareness of how they deploy their knowledge in practical situations, professionals can increase their capacities of learning in a more timely way. Understanding how they frame situations and ideas h