What is Machine learning: Definition and 83 Discussions
Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers; but not all machine learning is statistical learning. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.
Hello!
I wish to understand which lines and vertices in different 2D orthographic views of a 3D object correspond to each other. This information would also later be used to construct a 3D model from the 2D orthographic views.
Blue shows matched edges/lines. Orange shows matched...
Hi guys,
I was learning machine learning and I found something a bit confusing.
When I studied physics I saw the method of least squares to find the best parameters for the given data, in this case we assume we know the equation and we just minimize the error. So if it is a straight line model...
Hello, I am currently working on a paper that reqires me to simulate a renewable energy grid with machine learning. I'd be grateful if anyone with experience on this can give a few suggestion as to which software to use for this task. Thanks in advance.
I have a math degree and currently pursuing a BS in physics, I've been offered a job as a data scientist with occasional machine learning stuff.
1) Would this be helpful for my career in physics? If yes then how? (I'm aware that machine learning is being used in various areas of physics, but...
Hello forum,
I am reading this article on quantum machine learning. At one point in the article (page 7) they plot the ROC curve as background rejection vs. signal efficiency. Researching these concepts (since I did not understand them fully), I read that ROC curves should be plotted as TPR...
Musical notes
Can AI, Machine learning, Data science, Computer vision, image processing technologies assist in interpreting musical notes ?
Input dataset : Musical notes
Output : Sound (Audio file) played.
A python program which can assist in interpreting or converting text to speech ?.
The...
Olympic games logo
We have the beautiful logo designed for Olympic games. Five rings of different colours each representing the Continent. Significance of friendship between all continents. Countries representing the Olympic games from various continents.
Questions
1 The Origin of this Olympic...
Hi PF!
At the bottom of a transparent cup full of water is a hole where water drains. The cup is sloshed, so the gas-water interface is not flat. Are there any techniques you're aware of that implement machine learning to track the interface?
My current technique not using ML is:
1) splice...
I am currently pursuing my undergraduate studies in Physics. While I do know some programming (Java, MATLAB, Python), I have no knowledge in Machine Learning. This is an important field, and I want to study it. I have gone through some online courses, and need your help in determining which of...
Hi everyone,
I apologize to the mod if I posted in the wrong section.
For my exam of Machine Learning, I would like to implement a part of the work presented in this paper. In this work, the authors used two ML methods in cascade for forecasting Bitcoin. Starting from the initial data, they...
Summary:: I'm looking for some great books on deep learning related to image recognition that I can use in astrophysics.
Hello,
I'm about to start my master thesis, where I, in short, will be comparing snapshots of young binary stars from simulations to observations using deep learning -...
Can we trace all correct words from Jumbled words using machine learning prediction, search algorithms?
https://builtin.com/machine-learning/nlp-machine-learning
Input Dataset : Jumbled words.
Jumbled word example: oolp
Output : pool, loop, polo.
I wonder how to accurately perform data compression on the m x n matrix X using PCA. Each row is a data point, and each column is a feature.
So m data points with n features. If I like to go k < n dimensions, how is the correct way of doing so? How to I accurately create the matrix W_k, which...
Can anyone provide a good introductory reference to machine learning. Right now, I am interested in understanding the advantages and disadvantages of the various ML methods. I am not currently interested in detailed descriptions of their implementation.
I am probably going to have to brush up...
Long story short, my career goals are to work as a researcher/research scientist in machine learning or to develop models/algorithms for forecasting such as in financial markets or otherwise. Fundamentally, I find think this is quite similar to physics as a science, but that is for another...
In a collaboration of several institutions researchers have been able to record the thoughts of a person with a brain stem stroke resulting in anarthria (an inability to articulate speech) by mplanting network of electrodes in the brain and using a machine learning algorithm to decode the...
Hello everyone,
I am currently a master student working on physics and neural networks. I have already started producing neural network results (I use tensorflow and keras) so I know how to program the basic things that I am required to do, the problem is that I do not understand them well.
I...
Summary:: Hello there, I'm a mechanical engineer pursuing my graduate degree and I'm taking a class on machine learning. Coding is a skill of mine, but statistics is not... anyway, I have a homework problem on Bernoulli and Bayesian probabilities. I believe I've done the first few parts...
Say a person is infected with disease X. I suspect that it's possible for computers to learn the behavior of X via machine learning. This would lead to greater understanding of X. The unsupervised learning approach is pertinent to this.
For another approach, say a new medicine Y is being...
Hello everyone,
My question for this thread concerns the application of (mainly) mathematical analysis to fields such as signal processing and machine learning. More specifically, I was wondering if you happen to know of some interesting application of things like measure theory or functional...
I understand everything in this equation except for the summation. I understand it's the average error over the sample. But why do we need the "1"? Moreover wouldn't the error be the absolute value of the hypothesized value minus the concept value? Meaning
| h( x_i ) - c( x_i ) |
because you...
Hello,
I started studying machine learning and its basics. One of the applications of supervised learning ML is classification, i.e. identifying different objects and classify them. Being supervised means that the ML algorithm is initially served labelled data. The labelled data is used to a)...
Hey guys, I want to build a strong and straight plan for my next years of studying and once finish I am able to do something on my own and come up with crazy ideas and actually test them, build some awesome algorithms, all that cool stuff, but I'm kinda stumble so it would be nice if someone...
I am planing to change my field (in PhD) and learn Machine Learning to differentiate different phases of strongly correlated matter. I learned Monte Carlo method in my MS and have intermediate level knowledge of topological insulators.
Before completely getting into Machine Learning, I want to...
Hello,
I am trying to wrap my head around the difference between machine learning and statistics for predictive purposes and interpretability...
Is there a sharp difference between the two in terms of predictive power? I understand how machine learning needs to be first trained with data to...
Hello everybody. I am currently on my last year of Computational Physics education. More and more I am interested in Machine Learning and Neural Network. Time has come for me to choose diploma work thesis, so I am searching for interesting ideas where I can merge my interest in neural networks...
In Keras, the method model.fit() is used to train the neural network. How can I get the output from any hidden layer during training? Consider following code where neural network is trained to add two time series
#multivariate data preparation
#multivariate multiple input cnn example
from numpy...
Hi, I want to try out a bit of machine learning or deep learning with an optimisation problem in the lab. However, I'm confused at what the best option would even be or whether my optimisation problem is even applicable to either.
Firstly, the lab set up hasn't been built yet, I am computing...
Hello,
I am a Belgian master in physics and astronomy with a couple of years of working experience under his belt, who recently decided to obtain a second master in statistics. Normally I should graduate in 2020. My goal is to become a data scientist, but preferably within a scientific context...
I know I'm not that bright and I realize that this is a silly question to anyone in the field, but I was curious what the reward is in reinforcement learning algorithms.
I understand the concept behind reinforcement learning, though I am unsure of how you could program a reward into a program...
Hello, I am a second year PhD student in mathematics currently studying logic. I have recently begun questioning staying in the field, and I really think I may want to work in Machine Learning, at the very least in the theory of it. I am willing to self study from machine learning books, but...
Nowadays, the machine learning of computer science is hot. It is based on data, and drove by data. Thus, a question is naturally coming out: the data in physics, and the models of data. I think it is a really empirical way to know how physicists do the same thing as the computer scientists. So...
I wanted to go through Calculus and then Linear Algebra following either of two paths:
a) Keisler's Infinitesmal approach>>>Nitecki Deconstructing Calculus>>>Nitecki Calculus in 3D>>>Freidberg's Linear Algebra
OR
b) Simmons Calculus with analytic geometry>>>Apostol Vol 1>>>>Apostol Vol...
Hi everyone,
I am a software developer (bachelor's degree in Europe, different than a bachelor's degree in the US I believe) and I don't have a strong math/physics background but I am willing to learn.
For a few years now, I have been really interested in machine learning but until now, I only...
For one of my current projects in computer vision (which really is a study in point clustering and tracking in a data stream of n-dimensional data), I have come up with a way to very quickly index a 2D angle between two points or the angle of a vector. Doing a little bit of investigation, and...
I have always been on the skeptical, but not dismissive, end of judgments about achievemnts and rate of progress in this field. However, the following (please read through carefully) just completely blows my mind:
https://en.chessbase.com/post/the-future-is-here-alphazero-learns-chess
A self...
Hello,
I am new to both Python and machine learning. I am trying to learn by reading books on machine learning using Python. I started with this book. I could manage the first 3 chapters (although I had to dissect the codes on Github because not all the codes are included and explained in the...
I want to do a project using machine learning on the calorimeter event data of the LHCb. How can I access this data? Is it very difficult to navigate your way through the source code on your own?
In which fields of physics can we apply machine learning concepts? If I take data science master's with minor in particle physics or astrophysics, what are the possibilities that I would be employed as a physics related data scientist? (I am a programmer and I would like to change my career...
ArsTechnica recently published this online article on using quantum computing to identify Higgs interactions in LHC data having simulated many possible higgs interactions to train the model with 36 attributes...
Hello, recently I've become interested in learning more about machine learning, and am looking for a good textbook to study from. My background: Finished most of the required courses for my physics BSc and the related math courses. Some relevant courses I have taken:
Linear Algebra
Calculus...
Hi, I am confused about which to choose between computational physics and data science. Actually I am a computer science undergraduate and I have 4 years of experience in data mining. I discovered my interest in physics, so I planned to do a graduate degree with the focus of physics. But the...
I am a programming novice with minimal knowledge of C, Python and Java that I've taught myself from books and videos. Eventually I'd like to get into AI and machine learning but I have no idea where to start. Thus far, the minimal coding I've learned has been rather aimless with no specific...
My goal is to do research in Machine Learning (ML) and Reinforcement Learning (RL) in particular.
The problem with my field is that it's hugely multidisciplinary and it's not entirely clear what one should study on the mathematical side apart from multivariable calculus, linear algebra...
Hello everyone, I'm on my last undergraduation year in Physics and I've been asking myself what specific area to work with . Two months ago I've been studying Machine Learning and it amazed me so much that i push myself to come here and ask your opinions about it : There's a way to work deeply...
I just finished Andrew Ng's Machine Learning course on Coursera. I would highly recommend this course to anyone interested in data science or in applying machine learning concepts to real-world problems. He not only explains the algorithms at just the right level, but shows you the wisdom of...
From what I understand, machine learning is incredibly good at making predictions from data in a very automated/algorithmic way.
But for any inference that is going to deal with ideas of causality, it's primarily a subject matter concern, which relies on mostly on judgment calls and intuition...