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Mathematical Methods for Quantitative Finance (CourseraArchive) 2 个评论 关注 开始时间: 04/22/2022 持续时间: 8 weeks 主页: https://www.coursera.org/course/mathematicalmethods 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. |
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Data Analysis and Statistical Inference (CourseraArchive) 1 个评论 关注 开始时间: 04/22/2022 持续时间: Unknown 主页: https://www.coursera.org/course/statistics 简介: This course introduces you to the discipline of statistics as a science of understanding and analyzing data. You will learn how to effectively make use of data in the face of uncertainty: how to collect data, how to analyze data, and how to use data to make inferences and conclusions about real world phenomena. |
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CVX101: Convex Optimization (Stanford Online) 1 个评论 关注 开始时间: 04/22/2022 持续时间: 未知 主页: https://class.stanford.edu/courses/Engineering/CVX101/Winter2014/about 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. |
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StatLearning: Statistical Learning (Stanford Online) 3 个评论 关注 开始时间: 04/22/2022 持续时间: 未知 主页: https://class.stanford.edu/courses/HumanitiesScience/StatLearning/Winter2014/about 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). |
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Linux System Administration Essentials (EdxArchive) 2 个评论 关注 开始时间: 04/22/2022 持续时间: 未知 主页: https://www.edx.org/archive/linux-system-administration-essentials-linuxfoundationx-lfs201x 简介: Master the skills and get Linux certified for in-demand sysadmin jobs. |
