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Natural Language Processing (Coursera) 7 个评论 关注

开始时间: 02/24/2013 持续时间: 10 weeks


简介: Have you ever wondered how to build a system that automatically translates between languages? Or a system that can understand natural language instructions from a human? This class will cover the fundamentals of mathematical and computational models of language, and the application of these models to key problems in natural language processing.

Information Theory (Coursera) 1 个评论 关注

开始时间: 待定 持续时间: Unknown


简介: This course is an introduction to information theory, which emphasizes fundamental concepts as well as analytical techniques. Specific topics include: Information Measures, The I-Measure, Zero-Error Data Compression, Weak Typicality, Strong Typicality, Discrete Memoryless Channels, etc.

Probabilistic Graphical Models (Coursera) 5 个评论 关注

开始时间: 04/08/2013 持续时间: 11 weeks


简介: In this class, you will learn the basics of the PGM representation and how to construct them, using both human knowledge and machine learning techniques.

Neural Networks for Machine Learning (Coursera) 5 个评论 关注

开始时间: 待定 持续时间: 8 weeks


简介: Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well.

Mathematical Methods for Quantitative Finance (Coursera) 2 个评论 关注

开始时间: 06/01/2015 持续时间: 8 weeks


简介: Mathematical Methods for Quantitative Finance covers topics from calculus and linear algebra that are fundamental for the study of mathematical finance. Students successfully completing this course will be mathematically well prepared to study quantitative finance at the graduate level.

StatLearning: Statistical Learning (Stanford Online) 3 个评论 关注

开始时间: 01/20/2014 持续时间: 未知


简介: This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).

CVX101: Convex Optimization (Stanford Online) 1 个评论 关注

开始时间: 01/20/2014 持续时间: 未知


简介: This course concentrates on recognizing and solving convex optimization problems that arise in applications. The syllabus includes: convex sets, functions, and optimization problems; basics of convex analysis; least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems; optimality conditions, duality theory, theorems of alternative, and applications; interior-point methods; applications to signal processing, statistics and machine learning, control and mechanical engineering, digital and analog circuit design, and finance.

Compilers (Coursera) 2 个评论 关注

开始时间: 03/17/2014 持续时间: 11 weeks


简介: This course will discuss the major ideas used today in the implementation of programming language compilers. You will learn how a program written in a high-level language designed for humans is systematically translated into a program written in low-level assembly more suited to machines!