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Courses from Massachusetts Institute of Technology (free)

Elementary courses: Introduction to Probability and Statistics; Statistics for Applications. Advanced courses: Introduction to Algorithms; Time Series Analysis; Statistical Learning - Theory and Applications; Pattern Recognition for Machine Vision; Biomedical Signal and Image Processing; Parallel programming for Multicore Machines Using OpenMP and MPI
Statistics for Applications (MIT)
Part of MIT's Open Courseware program. This course is a broad treatment of statistics, concentrating on specific statistical techniques used in science and industry. Topics include: hypothesis testing and estimation, confidence intervals, chi-square tests, nonparametric statistics, analysis of variance, regression, correlation, decision theory, and Bayesian statistics.
Introduction to Algorithms (MIT)
Part of MIT's Open Courseware program, this course covers techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics covered include: sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; amortized analysis; graph algorithms; shortest paths; network flow; computational geometry; number-theoretic algorithms; polynomial and matrix calculations; caching; and parallel computing.
Introduction to Probability and Statistics (MIT)
Part of MIT's Open Courseware, this course provides an elementary introduction to probability and statistics with applications. Topics include: basic probability models; combinatorics; random variables; discrete and continuous probability distributions; statistical estimation and testing; confidence intervals; and an introduction to linear regression.
Time Series Analysis (MIT)
Part of MIT's Open Courseware, this course provides a survey of the theory and application of time series methods in econometrics. Topics covered will include univariate stationary and non-stationary models, vector autoregressions, frequency domain methods, models for estimation and inference in persistent time series, and structural breaks. We will cover different methods of estimation and inferences of modern dynamic stochastic general equilibrium models (DSGE): simulated method of moments, maximum likelihood and Bayesian approach. The empirical applications in the course will be drawn primarily from macroeconomics.
Statistical Learning Theory and Applications (MIT)
Part of MIT's Open Courseware, 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.
Pattern Recognition for Machine Vision (MIT)
Part of MIT's Open Courseware, 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.
Biomedical Signal and Image Processing (MIT)
Part of MIT's Open Courseware, this course presents the fundamentals of digital signal processing with particular emphasis on problems in biomedical research and clinical medicine. It covers principles and algorithms for processing both deterministic and random signals. Topics include data acquisition, imaging, filtering, coding, feature extraction, and modeling. The focus of the course is a series of labs that provide practical experience in processing physiological data, with examples from cardiology, speech processing, and medical imaging. The labs are done in MATLABĀ® during weekly lab sessions that take place in an electronic classroom.
Parallel Programming for Multicore Machines Using OpenMP and MPI (MIT)
Part of MIT's Open Courseware, this course introduces fundamentals of shared and distributed memory programming, teaches you how to code using openMP and MPI respectively, and provides hands-on experience of parallel computing geared towards numerical applications.