Automata (CourseraArchive) 3 个评论 关注

开始时间: 04/22/2022 持续时间: 6 weeks

主页: https://www.coursera.org/course/automata

简介: This course covers finite automata, context-free grammars, Turing machines, undecidable problems, and intractable problems (NP-completeness).

Coding the Matrix: Linear Algebra through Computer Science Applications (CourseraArchive) 9 个评论 关注

开始时间: 04/22/2022 持续时间: 10 weeks

主页: https://www.coursera.org/course/matrix

简介: Learn the concepts and methods of linear algebra, and how to use them to think about computational problems arising in computer science. Coursework includes building on the concepts to write small programs and run them on real data.

Machine Learning (CourseraArchive) 7 个评论 关注

开始时间: 04/22/2022 持续时间: Unknown

主页: https://www.coursera.org/course/machlearning

简介: Why write programs when the computer can instead learn them from data? In this class you will learn how to make this happen, from the simplest machine learning algorithms to quite sophisticated ones. Enjoy!

Artificial Intelligence Planning (CourseraArchive) 1 个评论 关注

开始时间: 04/22/2022 持续时间: 7 weeks

主页: https://www.coursera.org/course/aiplan

简介: The course aims to provide a foundation in artificial intelligence techniques for planning, with an overview of the wide spectrum of different problems and approaches, including their underlying theory and their applications.

Introduction to Psychology as a Science (CourseraArchive) 0 个评论 关注

开始时间: 04/22/2022 持续时间: Unknown

主页: https://www.coursera.org/course/psy

简介: Learn about how psychology has developed a body of knowledge about behavior and mind through the use of scientific methods. All areas of psychology will be covered.

Machine Learning (CourseraArchive) 29 个评论 关注

开始时间: 04/22/2022 持续时间: Unknown

主页: https://www.coursera.org/course/ml

简介: Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.

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).

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.

Financial Markets (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/financialmarkets

简介: An overview of the ideas, methods, and institutions that permit human society to manage risks and foster enterprise.

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.