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]