I have a dataset of 300 respondents (hours studied vs grade), I load the dataset in Excel run the data analysis add-in and run a linear regression. I get my results.
So the question is, Am I doing a Statistical Analysis or Am I doing Machine Learning? I know the question may seem simple but I think we should get some debate from this.
Maybe your question is better suited for Data Science as it is not a question related to app/program development. Running formulas in excel through an add on is not really considered anywhere close to "programming".
Statistical Analysis is when you take statistical metrics of your data, like mean, standard deviation, confidence intervall, p-value...
Supervised Machine Learning is when you try to classify or predict something. For these problemns you use features as input to the model in order to classify a class or predict a value.
In this case you are doing machine learning, because you use the hours studied feature to predict the student grade.
In the proper context, you're actually doing Statistical Analysis... (Which is part of Machine Learn
If by Machine Learning (ML) we mean any program that learns from data, then, yes, regression can be said to be part of ML. But there are several other aspects to Machine Learning such as : solution is improved iteratively based on some performance measure. Whereas for linear regression there is a closed form solution in the form of a direct formula using which all the parameters can be determined and it does not involve iterations. But there is other version of parameter estimation for regression that makes use of gradient descent and it involves several iterations. Does it mean that this iterative version of parameter estimation for regression is done forcefully to bring regression under machine learning umbrella? Or the iterative version has some advantages that the direct formula does not offer?
I won't comment on whether regression is part of ML or not (I don't really see where your definitions came from). But regarding the advantage of an iterative approach, please note that the closed-form solution for linear regression is as follows:
Where X is your design matrix.
Please note that inverting a matrix is an O(n^3) operation, which is infeasible for large n. This is the obvious advantage of the iterative approach using GD.
Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 25 days ago.
Improve this question
What's the difference between reinforcement learning, deep learning, and deep reinforcement learning? Where does Q-learning fit in?
Reinforcement learning is about teaching an agent to navigate an environment using rewards. Q-learning is one of the primary reinforcement learning methods.
Deep learning uses neural networks to achieve a certain goal, such as recognizing letters and words from images.
Deep reinforcement learning is a combination of the two, using Q-learning as a base. But instead of using actual state-value pairs, this is often used in environments where the state-action space is so large that it would take too long for Q-learning to converge. By using neural networks, we can find other state-action pairs that are similar. This “function approximation” allows effective learning in environments with very large state-action spaces.
Deep learning is a method using neural networks to make function approximators to solve various problems.
Ex: Learning a function which takes an image as input and output the bounding boxes of objects in the image.
Reinforcement learning is a field in which we have an agent and we want that agent to perform a task i.e, goal based problems where we use trial and error learning methods.
Ex: Agent learning to move from one position on grid world to a goal position without falling in a pit present in between.
Deep reinforcement learning is a way to solve goal based problems using neural networks. This is because, when we want agents to perform task in real world or current games, the state space is very big.
It takes agent very long time to even visit each state once and we cannot use look up tables to store the value functions.
So, to tackle this problem we use neural networks to approximate the state to generalize the learning process
Ex: We use DQN to solve many atari games.
Q-learning : It is a temporal difference learning method, where we have a Q-table to look for best action possible in the current state based on Q value function.
For learning Q values we use the reward and the maximum possible next state Q value.
Q-learning basically falls under Reinforcement learning and its deep reinforcement learning analog is Deep Q network (DQN).
The goal of machine learning methods is to learn rules from data and make predictions and/or decisions based on them.
The learning process can be done in a(n) supervised, semi-supervised, unsupervised, reinforcement learning fashion.
In reinforcement learning (RL), an agent interacts with an environment and learns an optimal policy, by trial and error (using reward points for successful actions and penalties for errors). It is used in sequential decision making problems [1].
Deep learning as a sub-field of machine learning is a mathematical framework for learning latent rules in the data or new representations of the data at hand. The term "deep" refer to the number of learning layers in the framework. Deep learning can be used with any of aforementioned learning strategies, i.e., supervised, semi-supervised, unsupervised, and reinforcement learning.
A deep reinforcement learning technique is obtained when deep learning is utilized by any of the components of reinforcement learning [1]. Note that Q-learning is a component of RL used to tell an agent that what action needs to be taken in what situation. Detailed information can be found in [1].
[1] Li, Yuxi. "Deep reinforcement learning: An overview." arXiv preprint arXiv:1701.07274 (2017).
Reinforcement learning refers to finish -oriented algorithms, which learn how to attain a coordination compound objective (goal) or maximize along a particular dimension over many steps. The basic theme behind Reinforcement learning is that an agentive role will learn from the environment by interacting with it and getting rewards for performing actions.
Deep Learning uses multiple layers of nonlinear processing units to extract feature and transformation
Deep Reinforcement Learning approach introduces deep neural networks to solve Reinforcement Learning problems thus they are named “deep.”
There's more distinction between reinforcement learning and supervised learning, both of which can use deep neural networks aka deep learning. In supervised learning - training set is labeled by a human (e.g. AlphaGo). In reinforcement learning (e.g. AlphaZero)- the algorithm is self-taught.
To put it in simple words,
Deep Learning - It's uses the model of neural network(mimicking the brain , neurons) and deep learning is used in image classification , data analyzing and in reinforcement learning too.
Reinforcement learning - This is a branch of machine learning, that revolves around an agent (ex: clearing robot) taking actions(ex: moving around searching trash) in it's environment(ex:home) and getting rewards(ex: collecting trash)
Deep-Reinforcement learning - This is one among the list of algorithms reinforcement learning has , this algorithm utilizes deep learning concepts.
Reinforcement learning (RL) is a type of machine learning that is mainly motivated by the feedback control of systems. RL is usually considered a type of optimal control that learns through interacting with a system/environment and getting feedback. RL usually replaces the computationally expensive dynamic programming methods with single time-step/multi time-step learning rule. Popular temporal difference methods in RL are considered somewhere in between dynamic programming and monte carlo methods. Classic RL methods use tabular algorithms that are not that scalable.
Deep learning (DL) is considered crucial part of modern machine learning (classical machine learning usually mean SVM, liner regression etc.). DL uses deep multilayered neural networks (NN) with backpropagation for learning. By using well designed deep NN networks complex input-output relations can be learned. Because of this property of approximating very complex functions DL have been extremely popular in recent years (2010-ish), especially in natural language tasks and computer vision tasks. One of the attractive aspect of DL is that these models can be end-to-end, meaning we do not need to do manual feature engineering. There are numerous types of DL algorithms, like Deep neural networs, convolutional neural networks, GRU, LSTM, GAN, attention, transfromer etc.
Deep RL uses deep NN architectures to replace the tabular methods for very high dimensional problems. Informally speaking, the controller is no longer a table look-up rather we use a deep NN as the controller. Because of leveraging deep NN in RL this is commonly known as deep RL.
roughly speaking:
deep learning uses deep neural networks to approximate complicated functions.
reinforcement learning is a branch in machine learning where your learner learns through interaction with environment. It is different from supervised or unsupervised learning.
if you use deep learning to approximate functions in reinforcement learning you call it deep reinforcement learning.
Reinforcement learning is a type of artificial intelligence that aims to model human-like decision-making. It's based on the idea that humans learn from their actions and reward themselves for doing things that are good, and punish themselves for doing things that are bad. Reinforcement learning algorithms try to replicate this process by changing the value of some variable in response to an action.
Deep learning is a type of machine learning model which uses multiple layers of processing to solve problems more effectively than traditional approaches. Deep learning models can be used for image recognition, speech recognition, and translation.
Deep reinforcement learning is a type of deep learning model that tries to solve problems by using sequences of actions called episodes to improve over time as well as by comparing results from different episodes. It's also known as Q-learning because it was first described by Richard Sutton in 1997 using the Q function (the fourth derivative).
Q-learning is a particular type of deep reinforcement learning algorithm that makes use of Q values (quantified measures) instead of actual rewards or penalties, which means it can be used without having access to real data or rewards/penalties yet still produce useful results
I've recently started learning machine learning algorithms. I've written a program in python from scratch to implement linear regression but I need some data pairs to use.
There are many dataset at internet to use,
have a look here, you can find many real datasets: uci
You can use scikit learn, It has some good in build dataset. You can refer this document.
I have two dependent continuous variables and i want to use their combined values to predict the value of a third binary variable. How do i go about discretizing/categorizing the values? I am not looking for clustering algorithms, i'm specifically interested in obtaining 'meaningful' discrete categories i can subsequently use in in a Bayesian classifier.
Pointers to papers, books, online courses, all very much appreciated!
That is the essence of machine learning and problem one of the most studied problem.
Least-square regression, logistic regression, SVM, random forest are widely used for this type of problem, which is called binary classification.
If your goal is to pragmatically classify your data, several libraries are available, like Scikits-learn in python and weka in java. They have a great documentation.
But if you want to understand what's the intrinsics of machine learning, just search (here or on google) for machine learning resources.
If you wanted to be a real nerd, generate a bunch of different possible discretizations and then train a classifier on it, and then characterize the discretizations by features and then run a classifier on that, and see what sort of discretizations are best!?
In general discretizing stuff is more of an art and having a good understanding of what the input variable ranges mean.