|
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. |
|
Computational Neuroscience (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/compneuro 简介: Understanding how the brain works is one of the fundamental challenges in science today. This course will introduce you to basic computational techniques for analyzing, modeling, and understanding the behavior of cells and circuits in the brain. You do not need to have any prior background in neuroscience to take this course. |
|
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. |
|
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. |
|
Introduction to Statistics (Udacity) 3 个评论 关注 开始时间: 04/22/2022 持续时间: 自主 主页: https://www.udacity.com/course/st101 简介: Statistics is about extracting meaning from data. In this class, we will introduce techniques for visualizing relationships in data and systematic techniques for understanding the relationships using mathematics. |
|
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! |
|
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. |
|
機器學習基石 (Machine Learning Foundations) (CourseraArchive) 10 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/ntumlone 简介: Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. The course teaches the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know. [機器學習旨在讓電腦能由資料中累積的經驗來自我進步。本課程將介紹各領域中的機器學習使用者都應該知道的基礎演算法、理論及實務工具。] |
|
数学之旅 The Journey of Mathematics (CourseraArchive) 0 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/sjtuma153 简介: 课程从问题开始揭示一些数学思想形成的过程，和听众一起从思想上重走一遍前辈们走过的路，体会数学抽象的魅力。 In this course I share the processes which formed the core concepts of mathematical philosophy, walking with students as they experience, learn and enjoy mathematical abstraction. |
|
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. |
