Courses tagged with "Infor" (117)
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 discusses topics such as boosting and feature selection and examines applications in several areas: Computer Vision, Computer Graphics, Text Classification, and Bioinformatics. The final projects, hands-on applications, and exercises are designed to illustrate the rapidly increasing practical uses of the techniques described throughout the course.
5.33 focuses on advanced experimentation, with particular emphasis on chemical synthesis and the fundamentals of quantum chemistry, illustrated through molecular spectroscopy. The written and oral presentation of experimental results is also emphasized in the course.
Acknowledgements
The materials for 5.33 reflect the work of many faculty members associated with this course over the years.
WARNING NOTICE
The experiments described in these materials are potentially hazardous and require a high level of safety training, special facilities and equipment, and supervision by appropriate individuals. You bear the sole responsibility, liability, and risk for the implementation of such safety procedures and measures. MIT shall have no responsibility, liability, or risk for the content or implementation of any of the material presented.
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