唐家声i 评论了课程: 機器學習技法 (Machine Learning Techniques)

2015-02-24 09:00

这门课算是结束了，因为太忙，证书拿得略带波折。
对这门课很有感情，因为第一门mooc证书就是一年前的机器学习基石，现在拿到了技法这门课的证书（等待最后发出来），也算是小圆满。
技法这门课，林老师讲授了很多有用的算法。本来讲到太多的算法，略有无头绪的感觉，但是这门课高质量的地方在于，林老师高度的概括和总结能力，几幅图，就把算法之间的关系和结构理清楚，按着这个结构，自己在头脑中过一遍全部内容，也会更加清晰。
作业方面，一如既往地难度高、压力大。很少会有mooc课程让我感受到这么大的作业压力。但是老师精心设计的作业，绝对会加深我们的理解。作业上，虽然有几个小错误，但是瑕不掩瑜。另外，第二次作业开始，每一次答题从以前的只给分数，变成了question level explaination了，这个设计着实让很多人松了口气...
林老师的课程理论一直很强，基石、技法皆如此。如果不只想做一个机器学习的爱好者，而是真正走进去的话，这两门课是不二选择。
最后，感谢林老师，没有这两门课，我也不会有机会获得现在的一些机会。
p.s. 看后期的论坛，发现drop掉的人还是蛮多的，这和基石第一次开课的时候一样。

唐家声i 评论了课程: Critical Thinking in Global Challenges

2014-03-03 09:18

总体上这门课程的难易度应该算是中等偏下。课程跨度五周，每周不需要花太多时间。选了这门课，并且听了Crafting an Effective Writer: Tools of the Trade这门课的内容。发现其实在外国人眼里，写作和批判性思维这两个话题，真正的内容和精华并不多（远没有那么多），关键在于训练，让大脑对于这样的思维方式形成反射，这恰恰是中国学生在学校教育中没有接受过和训练到的。所以，哪怕是一个foreigner都可以写出很好的东西。
这门课的作业，前三周都比较简单，评判形式也简单，就是选择题。不过尽量跟着题去思考。第四周如果选择的是population之类的话题，就可能会需要在讨论区针对某一话题的具体问题发表一个thread，要求就是以课上讲的批判性思维的内容来书写，实践对于知识的巩固会很好。第五周作业是可选内容，不做也不会影响最后的成绩。第五周同时开放最后的考试，也不难，课程通过还是很容易的。
最后，对于在准备GRE的同学，这门课可以帮你消除一些误解和对于批判性思维的恐惧感。

唐家声i 评论了课程: 機器學習基石 (Machine Learning Foundations)

2014-02-17 10:09

相比于其他的coursera上的课程，这门课难度还是很有的，开课时间还正好赶在学期末，压力不小，绝对不是消遣消遣就能完成作业的课。不过第一门课拿到了证书，算是开了个好头吧。期待林老师后面的课程~

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.

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。]

Introduction to Hadoop and MapReduce (Udacity) 1 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/ud617

简介: The Apache™ Hadoop® project develops open-source software for reliable, scalable, distributed computing. Learn the fundamental principles behind it, and how you can use its power to make sense of your Big 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!

Learning From Data (EdxArchive) 1 个评论 关注

开始时间: 04/22/2022 持续时间: 10 weeks

主页: https://www.edx.org/archive/learning-data-caltechx-cs1156x

简介: Introductory Machine Learning course covering theory, algorithms and applications. Our focus is on real understanding, not just "knowing."

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.

Algorithms: Design and Analysis, Part 1 (CourseraArchive) 5 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/algo

简介: In this course you will learn several fundamental principles of algorithm design: divide-and-conquer methods, graph algorithms, practical data structures (heaps, hash tables, search trees), randomized algorithms, and more.

Algorithms: Design and Analysis, Part 2 (CourseraArchive) 4 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/algo2

简介: In this course you will learn several fundamental principles of advanced algorithm design: greedy algorithms and applications; dynamic programming and applications; NP-completeness and what it means for the algorithm designer; the design and analysis of heuristics; and more.

Design of Computer Programs (Udacity) 4 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/cs212

简介: Learn new concepts, patterns, and methods that will expand your programming abilities, helping move you from a novice to an expert programmer.

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).