Machine learning to a Grandfather
*What is Machine Learning?
our ability to learn and get better at task through experience is part to be human when we born we know almost nothing and we can do almost nothing for ourselves but soon we are learning and become more capable every day but did you know that computer can do the same thing.
Machine learning brings together statistics and computer science to enable computers to learn how to do a given task without being programmed to do so. Just as your brain uses experience to improve a task so can computer.
Say you need a computer that can tell the difference between picture of a dog and a picture of a cat you could begin by feeding it images and telling it this one’s a dog, that one’s a cat.
a computer programmed to learn will seek statistical patterns within the data that will enable it to recognize a cat or a dog in the future it might figure it out on it’s own that cats have a shorter noses and a dogs come in large variety of sizes and then represent that information numerically organizing in space but crucially it’s the computer not the programmer that identifies those patterns and establishes the algorithm by which future data will be sorted.
One example of a simple yet highly effective algorithm is to find the optimal line separating cats from dogs.
When the computer sees a new picture it checks it which side of line it falls on and then says either cat or dog. But course there can be mistakes. The more data computer receives the more finely tunes its algorithm becomes and the more accurate it can be in its predictions. Machine learning is already widely applied.
It’s the technology behind facial recognition, text to speech recognition, spam filters on your inbox online shopping or viewing recommendations, credit card fraud detection and so much more…
At the university of Oxford machine learning researches are combining statistics and computer to build algorithms that can solve complex problems, more efficiently, using less computing power
From medical diagnosis to social media the potential of machine learning to transform our world is truly mind-blowing.
*Types of Machine Learning
1. Supervised Learning
Supervised learning describes a class of problem that involves using a model to learn a mapping between input examples and the target variable.
Applications in which the training data comprises examples of the input vectors along with their corresponding target vectors are known as supervised learning problems.
— Page 3, Pattern Recognition and Machine Learning, 2006.
Models are fit on training data comprised of inputs and outputs and used to make predictions on test sets where only the inputs are provided and the outputs from the model are compared to the withheld target variables and used to estimate the skill of the model.
Learning is a search through the space of possible hypotheses for one that will perform well, even on new examples beyond the training set. To measure the accuracy of a hypothesis we give it a test set of examples that are distinct from the training set.
— Page 695, Artificial Intelligence: A Modern Approach, 3rd edition, 2015.
There are two main types of supervised learning problems: they are classification that involves predicting a class label and regression that involves predicting a numerical value.
- Classification: Supervised learning problem that involves predicting a class label.
- Regression: Supervised learning problem that involves predicting a numerical label.
Both classification and regression problems may have one or more input variables and input variables may be any data type, such as numerical or categorical.
An example of a classification problem would be the MNIST handwritten digits dataset where the inputs are images of handwritten digits (pixel data) and the output is a class label for what digit the image represents (numbers 0 to 9).
An example of a regression problem would be the Boston house prices dataset where the inputs are variables that describe a neighborhood and the output is a house price in dollars.
Some machine learning algorithms are described as “supervised” machine learning algorithms as they are designed for supervised machine learning problems. Popular examples include: decision trees, support vector machines, and many more.
Our goal is to find a useful approximation f(x) to the function f(x) that underlies the predictive relationship between the inputs and outputs
— Page 28, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edition, 2016.
Algorithms are referred to as “supervised” because they learn by making predictions given examples of input data, and the models are supervised and corrected via an algorithm to better predict the expected target outputs in the training dataset.
The term supervised learning originates from the view of the target y being provided by an instructor or teacher who shows the machine learning system what to do.
— Page 105, Deep Learning, 2016.
Some algorithms may be specifically designed for classification (such as logistic regression) or regression (such as linear regression) and some may be used for both types of problems with minor modifications (such as artificial neural networks).
2. Unsupervised Learning
Unsupervised learning describes a class of problems that involves using a model to describe or extract relationships in data.
Compared to supervised learning, unsupervised learning operates upon only the input data without outputs or target variables. As such, unsupervised learning does not have a teacher correcting the model, as in the case of supervised learning.
In unsupervised learning, there is no instructor or teacher, and the algorithm must learn to make sense of the data without this guide.
— Page 105, Deep Learning, 2016.
There are many types of unsupervised learning, although there are two main problems that are often encountered by a practitioner: they are clustering that involves finding groups in the data and density estimation that involves summarizing the distribution of data.
- Clustering: Unsupervised learning problem that involves finding groups in data.
- Density Estimation: Unsupervised learning problem that involves summarizing the distribution of data.
An example of a clustering algorithm is k-Means where k refers to the number of clusters to discover in the data. An example of a density estimation algorithm is Kernel Density Estimation that involves using small groups of closely related data samples to estimate the distribution for new points in the problem space.
The most common unsupervised learning task is clustering: detecting potentially useful clusters of input examples. For example, a taxi agent might gradually develop a concept of “good traffic days” and “bad traffic days” without ever being given labeled examples of each by a teacher.
— Pages 694–695, Artificial Intelligence: A Modern Approach, 3rd edition, 2015.
Clustering and density estimation may be performed to learn about the patterns in the data.
Additional unsupervised methods may also be used, such as visualization that involves graphing or plotting data in different ways and projection methods that involves reducing the dimensionality of the data.
- Visualization: Unsupervised learning problem that involves creating plots of data.
- Projection: Unsupervised learning problem that involves creating lower-dimensional representations of data.
An example of a visualization technique would be a scatter plot matrix that creates one scatter plot of each pair of variables in the data-set. An example of a projection method would be Principal Component Analysis that involves summarizing a data-set in terms of eigenvalues and eigenvectors, with linear dependencies removed.
The goal in such unsupervised learning problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine the distribution of data within the input space, known as density estimation, or to project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization.
— Page 3, Pattern Recognition and Machine Learning, 2006.
3. Reinforcement Learning
Reinforcement learning describes a class of problems where an agent operates in an environment and must learn to operate using feedback.
Reinforcement learning is learning what to do — how to map situations to actions — so as to maximize a numerical reward signal. The learner is not told which actions to take, but instead must discover which actions yield the most reward by trying them.
— Page 1, Reinforcement Learning: An Introduction, 2nd edition, 2018.
The use of an environment means that there is no fixed training dataset, rather a goal or set of goals that an agent is required to achieve, actions they may perform, and feedback about performance toward the goal.
Some machine learning algorithms do not just experience a fixed dataset. For example, reinforcement learning algorithms interact with an environment, so there is a feedback loop between the learning system and its experiences.
— Page 105, Deep Learning, 2016.
It is similar to supervised learning in that the model has some response from which to learn, although the feedback may be delayed and statistically noisy, making it challenging for the agent or model to connect cause and effect.
An example of a reinforcement problem is playing a game where the agent has the goal of getting a high score and can make moves in the game and received feedback in terms of punishments or rewards.
In many complex domains, reinforcement learning is the only feasible way to train a program to perform at high levels. For example, in game playing, it is very hard for a human to provide accurate and consistent evaluations of large numbers of positions, which would be needed to train an evaluation function directly from examples. Instead, the program can be told when it has won or lost, and it can use this information to learn an evaluation function that gives reasonably accurate estimates of the probability of winning from any given position.
— Page 831, Artificial Intelligence: A Modern Approach, 3rd edition, 2015.
Impressive recent results include the use of reinforcement in Google’s AlphaGo in out-performing the world’s top Go player.
Some popular examples of reinforcement learning algorithms include Q-learning, temporal-difference learning, and deep reinforcement learning.
Hybrid Learning Problems
The lines between unsupervised and supervised learning is blurry, and there are many hybrid approaches that draw from each field of study.
In this section, we will take a closer look at some of the more common hybrid fields of study: semi-supervised, self-supervised, and multi-instance learning.
Theory
A core objective of a learner is to generalize from its experience. Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases.
History
1950 — Alan Turing creates the “Turing Test” to determine if a computer has real intelligence. To pass the test, a computer must be able to fool a human into believing it is also human.
1952 — Arthur Samuel wrote the first computer learning program. The program was the game of checkers, and the IBM computer improved at the game the more it played, studying which moves made up winning strategies and incorporating those moves into its program.
1957 — Frank Rosenblatt designed the first neural network for computers (the perceptron), which simulate the thought processes of the human brain.
1967 — The “nearest neighbor” algorithm was written, allowing computers to begin using very basic pattern recognition. This could be used to map a route for traveling salesmen, starting at a random city but ensuring they visit all cities during a short tour.
1979 — Students at Stanford University invent the “Stanford Cart” which can navigate obstacles in a room on its own.
1981 — Gerald Dejong introduces the concept of Explanation Based Learning (EBL), in which a computer analyses training data and creates a general rule it can follow by discarding unimportant data.
1985 — Terry Sejnowski invents NetTalk, which learns to pronounce words the same way a baby does.
1990s — Work on machine learning shifts from a knowledge-driven approach to a data-driven approach. Scientists begin creating programs for computers to analyze large amounts of data and draw conclusions — or “learn” — from the results.
1997 — IBM’s Deep Blue beats the world champion at chess.
2006 — Geoffrey Hinton coins the term “deep learning” to explain new algorithms that let computers “see” and distinguish objects and text in images and videos.
2010 — The Microsoft Kinect can track 20 human features at a rate of 30 times per second, allowing people to interact with the computer via movements and gestures.
2011 — IBM’s Watson beats its human competitors at Jeopardy.
2011 — Google Brain is developed, and its deep neural network can learn to discover and categorize objects much the way a cat does.
2012 — Google’s X Lab develops a machine learning algorithm that is able to autonomously browse YouTube videos to identify the videos that contain cats.
2014 — Facebook develops DeepFace, a software algorithm that is able to recognize or verify individuals on photos to the same level as humans can.
2015 — Amazon launches its own machine learning platform.
2015 — Microsoft creates the Distributed Machine Learning Toolkit, which enables the efficient distribution of machine learning problems across multiple computers.
2015 — Over 3,000 AI and Robotics researchers, endorsed by Stephen Hawking, Elon Musk and Steve Wozniak (among many others), sign an open letter warning of the danger of autonomous weapons which select and engage targets without human intervention.
2016 — Google’s artificial intelligence algorithm beats a professional player at the Chinese board game Go, which is considered the world’s most complex board game and is many times harder than chess. The AlphaGo algorithm developed by Google DeepMind managed to win five games out of five in the Go competition.
Future of Machine Learning!
Machine Learning (ML) is an application of AI (artificial intelligence) that allows systems to learn and improve without being programmed or supervised. If you are keen to know what is the future of Machine Learning, then you can read further to know more.
Being an intensively evolving language, continuous technological advancements are bound to hit the field of Machine Learning which are going to shape the future of Machine Learning. Let’s take a sneak peek into the future of Machine Learning in 2020.
