Statistical Machine Learning Lab

Princeton University

140.644.01 Practical Machine Learning

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Introduces popular Machine Learning methods and emphasizes their practical usage for data analysis. Acquaints students with methods to evaluate statistical machine learning models defined in terms of algorithms or function approximations using basic coverage of their statistical and computational theoretical underpinnings. Topics covered include: regression and prediction, tree-based methods, overview of supervised learning theory, support vector machines, kernel methods, ensemble methods, clustering, visualization of large datasets and graphical models. Examples of method applications covered include cancer prognosis from microarray data, visualization and analysis of social network data, and graphical models for clinical decision-making.

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ORF405 Regression and Applied Time Series

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Statistical Analysis of financial data: Density estimation, heavy tail distributions and dependence. Regression: linear, nonlinear, nonparametric. Time series analysis: classical models (AR, MA, ARMA, ..), state space systems and filtering, and stochastic volatility models (ARCH, GARCH, ....).

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ORF350 Analysis of Big Data

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The amount of data in our world has been exploding, and analyzing large data sets is becoming a central problem in our society. This course introduces the statistical principles and computational tools for analyzing big data: the process of exploring and predicting large datasets to find hidden patterns and gain deeper understanding, and of communicating the obtained results for maximal impact. Topics include massively parallel data management and data processing, model selection and regularization, statistical modeling and inference, scalable computational algorithms, descriptive and predictive analysis, and exploratory analysis.

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Reading group

We have biweekly reading group on every other Friday afternoon. The topics of this term include minimax lower bounds, empirical process, stochastic convex optimization, random matrix theory, and CUDA for GPU programming.

Get In Touch

Johns Hopkins University
Address: 615 N Wolfe St,
Room E3644
Phone: +410 955 3067
Fax: +410 955 0958
Email: hanliu@cs.jhu.edu