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- Abstract:
this course will give an introduction to basic datamining techniques. Advanced datamining techniques will be added later. The basic course will teach the theory behind and techniques for datamining. Author encourage the reader of this article to apply the techniques in real life data. The topics author want to cover are resectively clustering, self organizing maps, classification problems, regression tree, support vector machine, neural network, genetic algorithm, simulated anealing
- Subject:
- Science and Technology
- Grade Level:
- Post-secondary
- Collection:
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- Abstract:
Introduction to computational biology including the fundamentals of protein and nucleic acid sequence analysis, phylogenetic analysis, motif finding, hidden Markov models, and 3D structure prediction and modeling. An overview of emerging fields including expression profiling, quantitative image analysis and the modeling of cellular signal transduction networks are also included. Subject designed for advanced undergraduates and graduate students with strong backgrounds in either molecular biology or computer science but not necessarily both. Two self-study tracks are offered, introducing either basic statistical methods and programming (to biologists) or the fundamentals of molecular biology (to computer scientists). Lectures combine both perspectives to illustrate how computation is having a significant impact on modern biology. Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology.
- Subject:
- Science and Technology
- Grade Level:
- Post-secondary
- Collection:
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MIT OpenCourseWare
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Fundamentals of characterizing and recognizing patterns and features of interest in numerical data. Basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. Decision theory, statistical classification, maximum likelihood and Bayesian estimation, non-parametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research.
- Subject:
- Science and Technology
- Grade Level:
- Post-secondary
- Collection:
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MIT OpenCourseWare
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The applications of pattern recognition techniques to problems of machine vision is the main focus for this course. Topics covered include, an overview of problems of machine vision and pattern classification, image formation and processing, feature extraction from images, biological object recognition, bayesian decision theory, and clustering.
- Subject:
- Science and Technology
- Grade Level:
- Post-secondary
- Collection:
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MIT OpenCourseWare
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This is a new class on the topic of field (that is, 'in situ') and laboratory experiments in the social sciences - both what these experiments have taught and can teach us and how to conduct them.
- Subject:
- Social Sciences
- Grade Level:
- Post-secondary
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MIT OpenCourseWare
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Most algorithms in computer vision and image analysis can be understood in terms of two important components: a representation and a modeling/estimation algorithm. The representation defines what information is important about the objects and is used to describe them. The modeling techniques extract the information from images to instantiate the representation for the particular objects present in the scene. In this seminar, we will discuss popular representations (such as contours, level sets, deformation fields) and useful methods that allow us to extract and manipulate image information, including manifold fitting, markov random fields, expectation maximization, clustering and others. For each concept -- a new representation or an estimation algorithm -- a lecture on the mathematical foundations of the concept will be followed by a discussion of two or three relevant research papers in computer vision, medical and biological imaging, that use the concept in different ways. We will aim to understand the fundamental techniques and to recognize situations in which these techniques promise to improve the quality of the analysis.
- Subject:
- Science and Technology
- Grade Level:
- Post-secondary
- Collection:
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MIT OpenCourseWare
