기계 학습

개요

• One of the central challenges of computer science is to get a computer to do what needs to be done, without telling it how to do it.
• mathematical models are used to describe tasks.
• The parameters of these models are usually unknown and need to be estimated from experimental data using statistical methods.
• fundamental problems in machine learning
  • regression
  • classification
  • dimensionality reduction
  • probability density estimation

관련된 항목들

• 선형 회귀
• 나이브 베이즈 분류
• 마르코프 체인
• 마르코프 연쇄 몬테카를로 방법
• 은닉 마르코프 모델
• 칼만 필터
• 베이즈 네트워크
• K 근접 이웃
• 결정 트리
• 랜덤 포레스트
• 서포트 벡터 머신
• K-평균 알고리즘
• Gaussian mixture model
• DBSCAN
• 주성분 분석
• 특이값 분해
• Latent semantic analysis
• 잠재 디리클레 할당
• TFIDF
• EM 알고리즘
• 유전 프로그래밍
• Symbolic regression
• 합성곱 신경망
• 순환 인공 신경망
• Long Short-Term Memory
• Gated recurrent unit
• 생성적 적대 신경망
• neural network architectures
  • LeNet-5
  • AlexNet
  • VGGNet
  • GoogLeNet
  • Residual neural network

노트

• It is problems like this which machine learning is trying to solve.
• “Deep learning” – another hot topic buzzword – is simply machine learning which is derived from “deep” neural nets.
• Without a doubt, machine learning is proving itself to be a technology with far-reaching transformative powers.
• Machine learning is the key which has unlocked it, and its potential future applications are almost unlimited.
• Machine learning brings together computer science and statistics to harness that predictive power.
• The past decade has brought tremendous advances in an exciting dimension of artificial intelligence—machine learning.
• A prediction, in the context of machine learning, is an information output that comes from entering some data and running an algorithm.
• It can also be dangerously easy to introduce biases into machine learning, especially if multiple factors are in play.
• In many ways, building a sustainable business in machine learning is much like building a sustainable business in any industry.
• For example, machine learning can reveal customers who are likely to churn, likely fraudulent insurance claims, and more.
• Companies that effectively implement machine learning and other AI technologies gain a massive competitive advantage.
• Machine learning could also be used for security applications, such as analysing email communications or internet usage.
• The project will focus on current and near-term (5-10 years) applications of machine learning.
• The above definition encapsulates the ideal objective or ultimate aim of machine learning, as expressed by many researchers in the field.
• As with any concept, machine learning may have a slightly different definition, depending on whom you ask.
• – Stanford “Machine learning is based on algorithms that can learn from data without relying on rules-based programming.
• Emerj helps businesses get started with artificial intelligence and machine learning.
• Funding for research and development in the fields of machine learning and artificial intelligence is growing at a rapid pace.
• The field of machine learning is booming and having the right skills and experience can help you get a path to a lucrative career.
• From Apple to Google to Toyota, companies across the world are pouring resources into developing AI systems with machine learning.
• While there are different forms of AI, machine learning (ML) represents today's most widely valued mechanism for reaching intelligence.
• Machine learning became popular in the 1990s, and returned to the public eye when Google's DeepMind beat the world champion of Go in 2016.
• Since then, ML applications and machine learning's popularity have only increased.
• ML is one of the most exciting technologies that one would have ever come across.
• Machine learning is a branch of artificial intelligence that includes methods, or algorithms, for automatically creating models from data.
• Game-playing machine learning is strongly successful for checkers, chess, shogi, and Go, having beaten human world champions.
• Machine learning depends on a number of algorithms for turning a data set into a model.
• As with all machine learning, you need to check the predictions of the neural network against a separate test data set.
• A diverse community of developers, enterprises and researchers are using ML to solve challenging, real-world problems.
• Natural Language Derive insights from unstructured text using Google machine learning.
• Translation Dynamically translate between languages using Google machine learning.
• A very important group of algorithms for both supervised and unsupervised machine learning are neural networks.
• DeepMind continue to break new ground in the field of machine learning.
• But beyond these very visible manifestations of machine learning, systems are starting to find a use in just about every industry.
• Machine learning enables analysis of massive quantities of data.
• The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning.
• Machine learning involves computers discovering how they can perform tasks without being explicitly programmed to do so.
• As a scientific endeavor, machine learning grew out of the quest for artificial intelligence.
• However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning.
• In machine learning, a situation in which a model's predictions influence the training data for the same model or another model.
• More typically in machine learning, a hyperplane is the boundary separating a high-dimensional space.
• An i.i.d. is the ideal gas of machine learning—a useful mathematical construct but almost never exactly found in the real world.
• In machine learning, often refers to the process of making predictions by applying the trained model to unlabeled examples.
• The main intend of machine learning is to build a model that performs well on both the training set and the test set.
• Machine learning is the concept that a computer program can learn and adapt to new data without human intervention.
• The various data applications of machine learning are formed through a complex algorithm or source code built into the machine or computer.
• Machine learning is used in different sectors for various reasons.
• The supply of able ML designers has yet to catch up to this demand.
• Determining which inputs to use is an important part of ML design.
• The goal of ML is never to make “perfect” guesses, because ML deals in domains where there is no such thing.
• Fortunately, the iterative approach taken by ML systems is much more resilient in the face of such complexity.
• – Make revolutionary advances in machine learning and AI.
• Machine learning and AI are often discussed together, and the terms are sometimes used interchangeably, but they don’t mean the same thing.
• Machine learning and the technology around it are developing rapidly, and we're just beginning to scratch the surface of its capabilities.
• All ML tasks can be represented this way, or it's not an ML problem from the beginning.
• That's why selecting the right features usually takes way longer than all the other ML parts.
• That's why the phrase "will neural nets replace machine learning" sounds like "will the wheels replace cars".
• 1.1 Supervised Learning Classical machine learning is often divided into two categories – Supervised and Unsupervised Learning.
• This type of machine learning involves algorithms that train on unlabeled data.
• This approach to machine learning involves a mix of the two preceding types.
• Uses of machine learning Today, machine learning is used in a wide range of applications.
• But just because some industries have seen benefits doesn't mean machine learning is without its downsides.