sonach 评论了课程: Algorithms: Design and Analysis, Part 1

2014-12-05 22:54

非常赞，对思路的讲解非常到位！

Neural Networks for Machine Learning (CourseraArchive) 5 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/neuralnets

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.

Probabilistic Graphical Models (CourseraArchive) 5 个评论 关注

开始时间: 04/22/2022 持续时间: 11 weeks

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

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.

Discrete Optimization (CourseraArchive) 3 个评论 关注

开始时间: 04/22/2022 持续时间: 9 weeks

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

简介: Tired of solving Sudokus by hand? This class teaches you how to solve complex search problems with discrete optimization, including constraint programming, local search, and mixed-integer programming.

Natural Language Processing (CourseraArchive) 7 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/nlangp

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.

Linear and Integer Programming (CourseraArchive) 3 个评论 关注

开始时间: 04/22/2022 持续时间: Unknown

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

简介: This course will cover the very basic ideas in optimization. Topics include the basic theory and algorithms behind linear and integer linear programming along with some of the important applications. We will also explore the theory of convex polyhedra using linear programming.