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Questions tagged [machine-learning]

relates to algorithms that are "trained" by some data set.

Machine-learning relates to algorithms that improve themselves, either during runtime, or trained beforehand. Neural networks, eigenfaces and genetic algorithms are examples of techniques employed in machine-learning.

138 questions
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What math skills are required to learn machine learning?

I am interested in taking this online course on machine learning. As it stands my math is very elementary, and I am basically learning math from scratch on khan academy. Programming-wise I have a decent amount of experience, and a good overall…
levi
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Using machine learning to aim mirrors in a solar array?

I've been thinking about solar collectors where several independent mirrors to focus the light on a solar collector, similar to the following design from Energy Innovations. Because there will be flaws in the assembly of this solar array, I am…
Buttons840
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What specific category / type of machine learning can be used to make better AI decisions in this board game?

I'm collaborating with a friend to, as a challenge, create a bot that can beat the best human players at a browser-based card / board game called Duelyst. It looks like this: It was my original plan to build very basic machine learning from the…
john doe
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Reward discounting in reinforcement learning

I've been reading the Sutton and Barto book and following David Silver's lectures on youtube. The basic principles make a lot of sense to me and I've been building a maze (an arbitrary grid where the agent can move up, down, left right unless…
starfish
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How to use event weights in TMVA ROOT

I'm trying to train and perform classifiers from TMVA. My data set contains event weights besides event data. In the TMVA::Factory there's a method setWeightExpression to specify event weights. What are the event weights good for? Why it doesn't…
user2332057
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Redesigning Procedural Decision Engine To Leverage Machine Learning (and Which)

I'm not sure if this is the right forum, but I'm really looking for an advice on this. We've a procedural decision engine, which is fairly rudimentary, to decide application routing. It's basically multiple if-else statements (about 30 of them) in…
Simply_me
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What machine learning algorithm[s?] would be applicable here?

I have documents that contain an arbitrary amount of key/value fields. The value can be a primitive or a list. Completed documents all have an associated state (such as passed, failed, halted, etc.). Using completed documents as training data in a…
Jared Williams
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Predicting Diagnosis Codes

I am very new to Machine Learning. I've read about it and I successfully did a tutorial where we looked at the dimensions of Iris petals and predicted what other Iris petal sizes may be. This all made sense because we were just looking at…
Funkavenger
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Bernoulli naive bayes document model for multi-label classification

I want to ask about Bernoulli Naive Bayes document model. Can we use Bernoulli Naive Bayes document model for multi-label classification of documents?
star123
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How supervised or unsupervised machine learning algorithms get better over new datasets

Using this Coursera's course about machine learning, I learned some things about supervised and unsupervised algorithms. And from Wikipedia I've read that machine learning is "to get better performance/P at tasks/T with each task being done, that…
Saeed Neamati
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Is this a good task for machine learning - grouping pieces of DNA based on sequence?

Say I have a list of 50 DNA sequences all of the same length (6, 8 or 16 bps/ chars). I want to group these to sets of say 5 or 6 sequences per set. I have some criteria that need to be met based on the sequence: 1) There has to be at least three…
wobbily_col
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Machine Learning Algorithm for Heating/Lighting Optimization

I'm working on a project where I'm developing an interface that learns how you typically use a space, and tries to create the most appropriate control strategy for heating/lighting. I've done some research into the area of machine learning…
andyopayne
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Training data in unsupervised learning?

Unsupervised learning (USL) is about learning/constructing the algorithm to find the hidden data pattern based on training data without hard coded business rules like arithmetic sum, etc. Example of USL is grouping customers with similar online…
user3198603
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What do these terms refer to in the context of RL/TD learning?

I've been working on an implementation of TD-backgammon. The paper/project I'm basing my implementation on is here: https://www.cs.cornell.edu/boom/2001sp/Tsinteris/gammon.htm Everything makes sense to me up until the point that it talks about the…
Lee Hughs
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How to physically test a machine learning model?

I am new to machine learning. I have implemented a machine learning model which detects sound (for example: horn, siren, hammer etc) and predicts the type of sound. I have to physically test the model on actual sound of any object. How do I go about…
Neel Nath
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