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I have got a problem where I have to automate some task. Let's say switching on and off an appliance based on the user interaction with it on different days of the week.
Let's say we have a bulb B1 and a user U1. In beginning U1 will switch on and off the bulb B1 at times he wants. For example he may switch it on at 9:10 AM on Sunday and switch off at 11:15 AM on Sunday. Similarly if we divide 7-days into 5 time units we shall get 2,016 unit times. Our system has to learn user behavior against each 5-minutes unit and after 2 weeks of learning switch on or off the bulb as the user would have.
Do you think it can be accomplished with Re-enforcement based learning techniques? Any other machine-learning algorithm suggestion?
It is basically for a home automation with machine learning university project.
When reading your question, my first intuition was to try a Bayesian Inference approach using a uniform distribution over the unit times as prior and user behaviour as evidence. A good visual explanation can be found here. Also, you can have a look at this medium post.
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I'm doing software engineering and I'm learning Artificial Intelligence course in the current semester, then I need to give the project at the end of this semester (after 3 months). So, my question is which project is recommended for me, voice expression AI project or face expression AI project?
VOICE EXPRESSION: This software will listen user's conversation whole day then at the end of the day, it will show that how many bad words the user spoken, for how much time user got hyper, for how much time user got angry, etc.
FACE EXPRESSION: This software will monitor the screen of computer (for example teacher delivering lecture on ZOOM and camera of students are opened) then it will tell the user(or teacher) who is taking interest in user's lecture, who is confused, who wanna ask question etc.
So, If I'm a beginner in AI what project should I choose from those two projects? or should I choose easy project other than those two projects?
In principle, voice analysis seems to me easier than face analysis. To begin with, there is only one dimension, rather than two, and it would probably be easier to recognise words in a stream of sound than faces in a stream of images. However, I have a background in phonetics/signal processing, so sounds do look easier to me than images. If you've done image processing before, that might be better suited for you.
The key for a good project should not necessarily be how easy or hard it is, but whether it is something you are (a) interested in, (b) capable of achieving, and (c) relate to the course.
Also, be clear about what you want to achieve and how easy that is to determine: matching the sound pattern of a word is something much more objective than trying to identify if someone is bored or wants to ask a question based on facial expressions.
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is it possible to use logistic regression to identify prime numbers?
i´m trying to project a system with supervised logistic regression with a predefined database numbers and it´s classification (1 = Prime, 0 = Not Prime), using this data i want the computer to use this type of alghorythm to identify other numbers that aren´t classified on DB,
is it possible, or i´m trying to do something impossible?
Given the right network configuration and enough time, I don't know why it would be impossible.
It seems others have had success with different models and you might get a better idea from them:
Early success on prime number testing via artificial networks is presented in A Compositional Neural-network Solution to Prime-number Testing, László Egri, Thomas R. Shultz, 2006. The knowledge-based cascade-correlation (KBCC) network approach showed the most promise, although the practicality of this approach is eclipsed by other prime detection algorithms that usually begin by checking the least significant bit, immediately reducing the search by half, and then searching based other theorems and heuristics up to 𝑓𝑙𝑜𝑜𝑟(𝑥‾‾√). However the work was continued with Knowledge Based Learning with KBCC, Shultz et. al. 2006.
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I have set of transactions from a retail store. If I need to cluster the customers based on buying pattern I can do that using K-Means algorithm in Python.
How can I predict based on their earlier buying pattern, what are all products customers would be interested to buy in coming months?
I need to list products based on their choice of buying preference (high to low). What models or algorithm can be used for this?
Association Analysis is a text-book algorithm build for this use-case. You can also use Collaborative Filtering can also be used to model the problem that you've described.
Here is a Python implementation of Apriori Algorithm which I believe would help you
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I have a paragraph, system has to understand it and it should answer all the questions asked by the user. Please name the techniques and methodologies.
It all depends on the problem that you are trying to solve, the data available to you and the underlying domain. Lets get to it one by one:
Type of Problem
There are multiple types of question answering systems, like one word answers based on extract the exact answer from various sentences, or returning the most similar sentence from a list of sentences based on the question asked by the user, using various similarity and embedding techniques. I think this paper : Teaching Machines to Read and Comprehend should be a good place to start getting an idea about such systems.
Dataset
Next comes the dataset for such systems. Now there are various datasets available for question answering systems like :
SQuAD dataset
QA dataset based on Wikipedia Articles
Facebook bAbI dataset
AllenAI dataset based elementary Science question
NewsQA datset
Methodologies
Well there are multiple ways to go about solving this problem. It would be difficult to list all of them in one answer, but I can provide you some references:
Deep Learning for Question Answering
Various Deep Learning models on Question answering
SquAD dataset Leaderboard
Question Answering based on Word Alignment
Attention Based Question Answering
Reasoning-based QA
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(homework problem)
Which of the following problems are best suited for the learning approach?
Classifying numbers into primes and non-primes.
Detecting potential fraud in credit card charges.
Determining the time it would take a falling object to hit the ground.
Determining the optimal cycle for trafic lights in a busy intersection
I'm trying to answer your question without doing your homework.
Basically you can think of machine learning as a way to extract patterns from data where all other approaches fail.
So first clue here: If there is an analytic way to solve the problem then don't use machine learning! The analytic algorithm will likely be faster, more efficient, and 100% correct.
Second clue is: There has to be a pattern in the data. If you as a human see a pattern, machine learning can find it too. If lots of smart humans who are experts of the respective domain don't see a pattern then machine learning will most likely fail. Chaos can not be learned, i.e. classified/predicted.
That should answer your question. Make sure to also read the summary on wikipedia to get an idea whether a problem can be solved using supervised, unsupervised, or reinforcement learning.