Talk:Machine learning

Template:IEP assignment

Reinforcement Learning Placement

Shouldn't reinforcement learning be a subset of unsupervised learning?

I don't think so. Reinforcement learning is not completely unsupervised: the algorithm has access to a supervision signal (the reward). It's just that it is difficult to determine which action(s) led to the reward, and there's an exploitation vs. exploration tradeoff. So, it isn't strictly supervised learning, either. It's somewhere in-between. -- hike395 July 1, 2005 07:08 (UTC)

Definition by Samuel

The definition by Arthur Samuel,(1959) seems to be non-existent. Some papers/books cite his key-paper on ML in Checkers-games (see: http://aitopics.org/sites/default/files/classic/Feigenbaum_Feldman/Computers_And_Thought-Part_1_Checkers.pdf) but that doesn't contain a definition whatsoever (better yet, it states "While this is not the place to dwell on the importance of machine-learning procedures, or to discourse on the philosophical aspects" p.71). So I wonder whether we should keep that definition in the wiki-page... Otherwise I'm happy to receive the source+page where that definition is stated :)

Agree with above - this is a clear problem, as the WP leading quote can be found in many, many places around the Internet (as of 2017) with no actual citation. I've marked that reference as "disputed", since it doesn't cite any actual paper.

trimming or removing commercial software section

any thoughts by the *community* about hte relevance of some of the commercial software entries? i am thinking this list can be long if we start adding arbitrary software. i was wondering if people would be open to trimming the list or removing it all together. my thinking is that any prospective students should understand that this field is intense on mathematics, and while there is commercial appeal, much of the real work is done in the trenches.

things like google API and stuff can stay, obviously, but with the recent addition of a useless piece of software, i thought it'd be fruitful to have this discussion to prevent the list from growing.

there must be a healthy compromise that can be reached. — Preceding unsigned comment added by 174.3.155.181 (talk) 18:25, 19 April 2016 (UTC)

Numerical Optimization in Statistics might be a big mistake

This is because an optimizer constructed with sample data is a random variable, and the extreme value of the optimizer (minimum or maximun) cannot be more significant than other values of the optimizer. We should take the expectation of the optimizer to do statistical decision, e.g. model selection. Yuanfangdelang (talk) 19:59, 30 August 2016 (UTC)

Wikipedia is not a statistics journal. To discuss what statisticians should do, or should not do, is outside the scope of Wikipedia. Publish your opinion in relevant statistics journals instead and "fix" it there first. Wikipedia is an encyclopedia, which summarizes and references important prior work only and does not do original research. We literally do not care of what "might be a big mistake" (as long as it is a mistake common e.g. in literature): Wikipedia has an article on Flat Earth despite this being a "mistake" because it used to be a dominant concept. HelpUsStopSpam (talk) 09:48, 31 August 2016 (UTC)

Self-learning chip

There seem to be few chips on the market that are self-learning. There's at least one being manufactured today, see here KVDP (talk) 13:21, 9 May 2017 (UTC)