Courses tagged with "Nutrition" (287)
The goal of this course is to teach both the fundamentals of nuclear cell biology as well as the methodological and experimental approaches upon which they are based. Lectures and class discussions will cover the background and fundamental findings in a particular area of nuclear cell biology. The assigned readings will provide concrete examples of the experimental approaches and logic used to establish these findings. Some examples of topics include genome and systems biology, transcription, and gene expression.
Provides a comprehensive introduction to key issues and findings in object recognition in experimental, neural, computational, and applied domains. Emphasizes the problem of representation, exploring the issue of how 3-D objects should be encoded so as to efficiently recognize them from 2-D images. Second half focuses on face recognition, an ecologically important instance of the general object recognition problem. Describes experimental studies of human face recognition performance and recent attempts to mimic this ability in artificial computational systems.
The course, which spans two thirds of a semester, provides students with a research-inspired laboratory experience that introduces standard biochemical techniques in the context of investigating a current and exciting research topic, acquired resistance to the cancer drug Gleevec. Techniques include protein expression, purification, and gel analysis, PCR, site-directed mutagenesis, kinase activity assays, and protein structure viewing.
This class is part of the new laboratory curriculum in the MIT Department of Chemistry. Undergraduate Research-Inspired Experimental Chemistry Alternatives (URIECA) introduces students to cutting edge research topics in a modular format.
Acknowledgments
Development of this course was funded through an HHMI Professors grant to Professor Catherine L. Drennan.
The course includes survey and special topics designed for graduate students in the brain and cognitive sciences. It emphasizes ethological studies of natural behavior patterns and their analysis in laboratory work, with contributions from field biology (mammology, primatology), sociobiology, and comparative psychology. It stresses mammalian behavior but also includes major contributions from studies of other vertebrates and of invertebrates. It covers some applications of animal-behavior knowledge to neuropsychology and behavioral pharmacology.
The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory. It starts with a review of classical statistical techniques, including Regularization Theory in RKHS for multivariate function approximation from sparse data. Next, VC theory is discussed in detail and used to justify classification and regression techniques such as Regularization Networks and Support Vector Machines. Selected topics such as boosting, feature selection and multiclass classification will complete the theory part of the course. During the course we will examine applications of several learning techniques in areas such as computer vision, computer graphics, database search and time-series analysis and prediction. We will briefly discuss implications of learning theories for how the brain may learn from experience, focusing on the neurobiology of object recognition. We plan to emphasize hands-on applications and exercises, paralleling the rapidly increasing practical uses of the techniques described in the subject.
This series of research talks by members of the Department of Brain and Cognitive Sciences introduces students to different approaches to the study of the brain and mind.
Topics include:
- From Neurons to Neural Networks
- Prefrontal Cortex and the Neural Basis of Cognitive Control
- Hippocampal Memory Formation and the Role of Sleep
- The Formation of Internal Modes for Learning Motor Skills
- Look and See: How the Brain Selects Objects and Directs the Eyes
- How the Brain Wires Itself