- 主题发起人 gongbao
- 发布时间 2018-03-29
把各个program的教材找到，快速浏览一下，就知道学的是什么了。个人感觉要根据自己的目标就业方向选择。感觉Communication Systems专业性强，适用面比较窄，一般面向研究开发类公司。行业领先者可以用指头数。Machine Learning应用的领域应该更广泛，但也是面向专业研发类公司。这两个方向应该是专业牛人的首选。Software Engineering of Distributed Systems貌似适应面广一点。4面向金融，5不知到注重哪方面。完全是根据专业名称瞎猜，请不要参考。
最后编辑: 2018-03-31
我觉得你说的有道理，Machine Learning涉及的行业十分广泛。很多行业实施machine learning, 应该算是刚刚起步，需求应该十分强劲。现在Narrow AI十分火爆，大的科技公司都投入在应用广泛的领域，例如自动驾驶，物流，自动货物分拣之类。这些公司需要的是高端的人才，但也需要中级，初级一起工作才能形成一个团队。还有很多行业，大公司无暇顾及，或有行业门槛，比如医疗，矿业之类。从事Machine Learning，可以说进可攻，退可守。
个人觉得计算机对生产力的提高正在加速。十年前，AI还限于研究领域，辨别汽车牌号就很NB了，现在已经有越来越广泛的应用。下面的结构图，可以看出AI各个分支的相对关系。
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最后编辑: 2018-04-01
我不是专业人士,理解肤浅. 但觉得在就业方面machine learning很有前景. 一般要计算机和业务领域的结合. 按鸡哥的说法,交叉学科. Read the following, I get the following points. 我看到了这些关键字： 算法，统计，数据分析，业务领域知识。业务领域知识就是计算机所要结合的专业。例如语音识别，OCR, Intrusion protection, Financial ect.
我一开始认为machine learning是如何把传统的工业控制同强大的计算机系统关联，强化已有的系统功能，或开拓新的功能。比如自动驾驶，和其他self adaptive的系统。看来理解的不全面。
世界上大多数程序员都是在做所谓的信息管理系统。就是数据库，录入，处理，输出之类。和算法，统计，数据分析没有一毛钱关系。加拿大的联邦政府花20多亿搞砸了的Phoenix系统，网络销售之类，SAP, 会计，以及现在我们用的论坛都是这类系统。这些是平庸的程序员的首选。
https://en.wikipedia.org/wiki/Machine_learning
Machine learning is a field of computer science that gives computer systems the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.[1]
The name machine learning was coined in 1959 by Arthur Samuel.[2] Evolved from the study of pattern recognition and computational learning theory in artificial intelligence,[3] machine learning explores the study and construction of algorithms that can learn from and make predictions on data[4] – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,[5]:2 through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach,[6] optical character recognition (OCR),[7] learning to rank, and computer vision.
Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining,[8] where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning.[5]:vii[9] Machine learning can also be unsupervised[10] and be used to learn and establish baseline behavioral profiles for various entities[11] and then used to find meaningful anomalies.
Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data.[12]
Effective machine learning is difficult because finding patterns is hard and often not enough training data are available; as a result, machine-learning programs often fail to deliver.[13][14]
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