Machine Learning and Artificial Intelligence (AI) in Images & Pictures

Machine learning exmaples

Introduction to Machine Learning for AI

Intelligence

Intelligence: The notion of intelligence can be defined in many ways. Here we define it as the ability to take the right decisions, according to some criterion (for example, survival and reproduction, for most animals).

To take better decisions requires knowledge, in a form that is operational, therefore, can be used to interpret sensory data and use that information to take decisions.

Artificial Intelligence (AI)

By Artificial intelligence AI, we mean computers already possess some intelligence thanks to all the software/firmware programs that humans have crafted and which allow them to "do things" that we consider useful and that is basically what we mean for a computer to take the right decisions.

But there are many tasks which animals and humans are able to do rather easily but remain out of reach of computers, at the beginning of the 21st century.

Many of these tasks fall under the label of Artificial Intelligence, and include many perception and control tasks.

Why is it that we have failed to write software programs for these tasks? This website believe that it is mostly because we do not know explicitly (formally) how to do these tasks, even though our brain (coupled with a body) can do them.

Doing those tasks involve knowledge that is currently implicit, but we have information about those tasks through data and examples (e.g. observations of what a human would do given a particular request or input).

How do we get machines to acquire that kind of intelligence? Using data and examples to build operational knowledge is what learning is about.

Automated Ads

Automated ads (or automate ads) is artificial intelligence software that manages and optimizes your ads on autopilot to get the best possible results even if you have no idea what you're doing and are not even paying attention.

Automated Ads lets you...

  • Automatically "optimize" your ads using the advanced Artificial intelligence (AI)

  • Continue to then automatically adjust your ad campaigns / ad sets using some proprietary algorithms to grow the winners and stop the losers WITHOUT the risk of killing winning ad sets that normally occurs when people try to grow winning ad sets on their own (a common mistake).

  • Instantly analyze, for example, Facebook ads to see the bigger picture of what's working and what's not in a much better report than what Facebook gives you.

  • Ability to Automatically CREATE NEW ADS FROM SCRATCH (graphics, copy, etc. - you can still tweak them, but this saves you hours of work)

  • Integration with Top E-commerce Store and Custom T-Shirt, Mugs, Jewellry Stores (so you can have it automatically turn your shopify store into lots of ads advertising each of your products).

  • Track and monitor it all in one easy place.

What is Machine Learning (ML)?

By definition, a Machine learning (ML) is a type of Artificial Intelligence (AI) that deals with the issue of how to build computer programs or software engineering (SE) programs that improve their performance at some tasks through experience.

In other words, a computer program is said to learn from experience with respect to some class of tasks and performance measurement, if its performance tasks is as measured and improves with experience.

Machine learning algorithms have proven to be of great practical value in a variety of application domains. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development, artificial intelligence (ai) researcher and maintenance tasks could be formulated as learning problems and approached in terms of learning algorithms.

This website http://www.ai-machine-learning.com (or machinelearning.com) deals with the subject of ai machine learning applications in software engineering.

It provides an overview ofmachine learning, summarizes the state-of-the-practice in this niche area, gives a classification of the existing work, and offers some application guidelines.

Also included in this website www.machinelearning.com an incredibly useful books and magazines titles and descriptions on ai machine learning.

Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past few years.

Hence, machine learning refers to a system capable of the autonomous acquisition and integration of knowledge.

Here we focus on a few concepts that are most relevant to this machine learning course.

Overview of Machine Learning

The field of ML includes: supervised learning, unsupervised learning and reinforcement learning.

ML algorithms have been utilized in many different problem domains. Some typical applications are:

Data mining problems where large databases contain valuable implicit regularities that can be discovered automatically, poorly understood domains where there is lack of knowledge needed to develop effective algorithms, or domains where programs must dynamically adapt to changing conditions.

Find below, list of publications and web sites (machinelearning.com, deep learning.com) offers a good starting point for the interested reader to be acquainted with the state-of-the-practice in machine learning applications.

Formalization of Learning

First, let us formalize the most common mathematical framework for learning.
Examples of hand-written digits taken from US zip codes
Consider the example of recognizing handwritten digits, illustrated in the above figure. Each digit corresponds to a 28x28 pixel image and so can be represented by a vector x comprising 784 real numbers.

The goal is to build a machine that will take such a vector x as input and that will produce the identity of the digit 0, . . . , 9 as the output. This is a nontrivial problem due to the wide variability of handwriting.

It could be tackled using handcrafted rules or heuristics for distinguishing the digits based on the shapes of the strokes, but in practice such an approach leads to a proliferation of rules and of exceptions to the rules and so on, and invariably gives poor results.

Far better results can be obtained by adopting a machine learning approach in which a large set of N digits {x1, . . . , xN} called a training set is used to tune the parameters of an adaptive model.

The categories of the digits in the training set are known in advance, typically by inspecting them individually and hand-labelling them.

We can express the category of a digit using target vector t, which represents the identity of the corresponding digit.

Suitable techniques for representing categories in terms of vectors will be discussed later. Note that there is one such target vector t for each digit image x.

The result of running the machine learning algorithm can be expressed as a function y(x) which takes a new digit image x as input and that generates an output vector y, encoded in the same way as the target vectors.

The precise form of the function y(x) is determined during the training phase, also known as the learning phase, on the basis of the training data.

Once the model is trained it can then determine the identity of new digit images, which are said to comprise a test set. The ability to categorize correctly new examples that differ from those used for training is known as generalization.

In practical applications, the variability of the input vectors will be such that the training data can comprise only a tiny fraction of all possible input vectors, and so generalization is a central goal in pattern recognition.

If we're given training examples: {\cal D} = \{z_1, z_2, \ldots, z_n\} with the z_i being examples sampled from an unknown process P(Z). We are also given a loss functional L which takes as argument a decision function f and an example z, and returns a real-valued scalar. We want to minimize the expected value of L(f,Z) under the unknown generating process P(Z).

Supervised learning

Supervised learning deals with learning a target function from training examples of its inputs and outputs. Also in supervised learning, each examples is an (input,target) pair: Z=(X,Y) and f takes an X as argument. The most common examples are:

  • Regression: Y is a real-valued scalar or vector, the output of f is in the same set of values as Y, and we often take as loss functional the squared error

    L(f,(X,Y)) = ||f(X) - Y||^2

  • Classification: Y is a finite integer (e.g. a symbol) corresponding to a class index, and we often take as loss function the negative conditional log-likelihood, with the interpretation that f_i(X) estimates P(Y=i|X)   L(f,(X,Y)) = -\log f_Y(X), where we have the constraints



Unsupervised learning

Unsupervised learning attempts to learn patterns in the input for which no output values are available. Also in unsupervised learning we are learning a function f which helps to characterize the unknown distribution P(Z).

Sometimes f is directly an estimator of P(Z) itself (this is called density estimation). In many other cases f is an attempt to characterize where the density concentrates.

Clustering algorithms divide up the input space in regions (often centered around a prototype example or centroid).

Some clustering algorithms create a hard partition (e.g. the k-means algorithm) while others construct a soft partition (e.g. a Gaussian mixture model) which assign to each Z a probability of belonging to each cluster.

Another kind of unsupervised learning algorithms are those that construct a new representation for Z. Many deep learning algorithms fall in this category, and so does Principal Components Analysis.

Reinforcement learning

Reinforcement learning is concerned with learning a control policy through reinforcement from an environment.

Local Generalization

By local generalization, the vast majority of learning algorithms exploit a single principle for achieving generalization: local generalization.

It assumes that if input example x_i is close to input example x_j, then the corresponding outputs f(x_i) and f(x_j) should also be close. This is basically the principle used to perform local interpolation.

This principle is very powerful, but it has limitations:

What if we have to extrapolate? or equivalently, what if the target unknown function has many more variations than the number of training examples?

In that case there is no way that local generalization will work, because we need at least as many examples as there are ups and downs of the target function, in order to cover those variations and be able to generalize by this principle.

This issue is deeply connected to the so-called curse of dimensionality for the following reason. When the input space is high-dimensional, it is easy for it to have a number of variations of interest that is exponential in the number of input dimensions.

For example, imagine that we want to distinguish between 10 different values of each input variable (each element of the input vector), and that we care about about all the 10^n configurations of these n variables.

Using only local generalization, we need to see at least one example of each of these 10^n configurations in order to be able to generalize to all of them.

Foundations and Trends in Machine Learning

The Foundations and trends in machine learning of theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions; For example, in vision, language, and other AI-level tasks, one may need deep architectures.

Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae.

Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas.

This website www.machineleaning.com, try to discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.

Allowing computer science technology to model our world well enough to exhibit what we call intelligence has been the focus of more than half a century of research.

To achieve this, it is clear that a large quantity of information about our world should somehow be stored, explicitly or implicitly, in the computer.

Because it seems daunting to formalize manually all that information in a form that computers can use to answer questions and generalize to new contexts, many researchers have turned to learning algorithms to capture a large fraction of that information.

Much progress has been made to understand and improve learning algorithms, but the challenge of artificial intelligence (AI) remains.

Do we have algorithms that can understand scenes and describe them in natural language? Not really, except in very limited settings.

Do we have algorithms that can infer enough semantic concepts to be able to interact with most humans using these concepts? No.

If we consider image understanding, one of the best specified of the AI tasks, we realize that we do not yet have learning algorithms that can discover the many visual and semantic concepts that would seem to be necessary to interpret most images on the web.

The situation is similar for other AI tasks.

The Promise and Limitations of Machine Learning with R

Ruslan Salakhutdinov

Ruslan Salakhutdinov or Russ Salakhutdinov is now an Apple Director (ruslan salakhutdinov apple) of Artificial Intelligence Machine Learning who happens to be the former Associate Professor in the MachineLearning Department, School of Computer Science at Carnegie Mellon University, Pittsburgh, Pennsylvania United States (US) gave presentaion on the promise and limitations of learning machine at MIT Technology Review.

Web Structure Mining

Web structure mining examples

What is mining for structure or data mining structure? According to [wikipedia], by definition, Structure mining or structured data mining is the process of finding and extracting useful information from semi-structured data sets. Graph mining, sequential pattern mining and molecule mining are special cases of structured data mining.

In contrast, web structure mining is one of three categories of web mining for data, is a tool used to identify the relationship between Web pages linked by information or direct link connection. This structure data is discoverable by the provision of web structure schema through database techniques for Web pages.

Today in 2017, we have a massive increase in both computational power and the amount of data. We have Images & Video; Text & Language; Speech & Audio; Product Recommendation; Relational Data & Social Network; fMRI, and Tumor region; etc... Most Data is Unlabeled.

DEEP LEARNING

By definition, according to [wikipedia] Deep learning or deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data.

Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.

Impact of Deep Learning

  • Speech Recognition - Companies: Microsoft, IBM
  • Computer Vision - Companies: Google, IBM
  • Recommender Systems - Companies: eBay, Netflix
  • Language Understanding - Companies: eBay, Netflix
  • Drug Discovery & Medical Image Analysis - Companies: Merck, Novartis
  • Learning Deep Architectures for AI

    The learning deep architectures for ai or deep learning architectures for ai methods aim at learning feature hierarchies with features from higher levels of the hierarchy formed by the composition of lower level features.

    Automatically learning features at multiple levels of abstraction allows a system to learn complex functions mapping the input to the output directly from data, without depending completely on human-crafted features.

    This is especially important for higher-level abstractions, which humans often do not know how to specify explicitly in terms of raw sensory input.

    The ability to automatically learn powerful features will become increasingly important as the amount of data and range of applications to machine learning methods continues to grow.

    Depth of architecture refers to the number of levels of composition of non-linear operations in the function learned.

    Whereas most current learning algorithms correspond to shallow architectures (1, 2 or 3 levels), the mammal brain is organized in a deep architecture with a given input percept represented at multiple levels of abstraction, each level corresponding to a different area of cortex.

    Humans often describe such concepts in hierarchical ways, with multiple levels of abstraction. The brain also appears to process information through multiple stages of transformation and representation.

    This is particularly clear in the primate visual system, with its sequence of processing stages: detection of edges, primitive shapes, and moving up to gradually more complex visual shapes.

    Ideally, we would like the raw input image to be transformed into gradually higher levels of representation, representing more and more abstract functions of the raw input, for example, edges, local shapes, object parts, etc.

    In practice, we do not know in advance what the "right" representation should be for all these levels of abstractions, although linguistic concepts might help guessing what the higher levels should implicitly represent.

    Inspired by the architectural depth of the brain, neural network researchers had wanted for decades to train deep multi-layer neural networks, but no successful attempts were reported before 2006 (Except for neural networks with a special structure called convolutional networks).

    Researchers reported positive experimental results with typically two or three levels (i.e. one or two hidden layers), but training deeper networks consistently yielded poorer results.

    Something that can be considered a breakthrough happened in 2006: When Hinton and collaborators at University of Toronto introduced Deep Belief Networks or DBNs for short, with a learning algorithm that greedily trains one layer at a time, exploiting an unsupervised learning algorithm for each layer, a Restricted Boltzmann Machine (RBM).

    Shortly after, related algorithms based on auto-encoders were proposed, apparently exploiting the same principle: guiding the training of intermediate levels of representation using unsupervised learning, which can be performed locally at each level.

    Other algorithms for deep architectures were proposed more recently that exploit neither RBMs nor auto-encoders and that exploit the same principle.

    Why is Machine Learning Important?

    1. Some tasks cannot be defined well, except by examples (for example, recognizing people).

    2. Relationships and correlations can be hidden within large amounts of data. Machine Learning/Data Mining may be able to find these relationships.

    3. Human designers often produce machines that do not work as well as desired in the environments in which they are used.

    4. The amount of knowledge available about certain tasks might be too large for explicit encoding by humans (for example, medical diagnostic).

    5. Environments change over time.

    6. New knowledge about tasks is constantly being discovered by humans. It may be difficult to continuously re-design systems "by hand".

    Areas of Influence for Machine Learning

    a. Adaptive Control Theory - How to deal with controlling a process having unknown parameters that must be estimated during operation?

    b. Artificial Intelligence - How to write algorithms to acquire the knowledge humans are able to acquire, at least, as well as humans?

    c. Brain Models - Non-linear elements with weighted inputs (Artificial Neural Networks) have been suggested as simple models of biological neurons.

    d. Evolutionary Models - How to model certain aspects of biological evolution to improve the performance of computer programs?

    e. Psychology - How to model human performance on various learning tasks?

    f. Statistics - How best to use samples drawn from unknown probability distributions to help decide from which distribution some new sample is drawn?

    A few useful things to know about machine learning. An Example of Designing a Learning System

    1. Machine Learning Problems Description
    2. Choosing the Machine Learning Training Experience
    3. Choosing the Machine Learning Cost Function Target
    4. Choosing a Representation for the Cost Function Target
    5. Choosing a Machine Learning Algorithms Cost Function Approximation
    6. Machine Learning Final Exam Solution Design

    1. Machine Learning Problems Description: A Checker Learning Problem

    An examples of machine learning problems or practice problems using a checker learning topic:

    • Task T - Playing Checkers
    • Performance Measure P - Percent of games won against opponents
    • Training Experience E - To be selected => Games Played against itself
    • 2. Choosing the Machine Learning Training Experience

      • Direct versus Indirect Experience - Indirect Experience gives rise to the credit assignment problem and is thus more difficult.

      • Teacher versus Learner Controlled Experience - The teacher might provide training examples; the learner might suggest interesting examples and ask the teacher for their outcome; or the learner can be completely on its own with no access to correct outcomes.

      • How Representative is the Experience? - Is the training experience representative of the task the system will actually have to solve? It is best if it is, but such a situation cannot systematically be achieved.

      3. Choosing the Machine Learning Cost Function Target

      • Given a set of legal moves, we want to learn how to choose the best move - Since the best move is not necessarily known, this is an optimization problem.

      • ChooseMove: B --> M is called a Target Function - ChooseMove, however, is difficult to learn. An easier and related target function to learn is V: B --> R, which assigns a numerical score to each board. The better the board, the higher the score.

      • Operational versus Non-Operational Description of a Target Function - An operational description must be given.

      • Function Approximation - The actual function can often not be learned and must be approximated.

      4. Choosing a Representation for the Cost Function Target

      • Expressiveness versus Training set size - The more expressive the representation of the target function, the closer to the "truth" we can get.

        However, the more expressive the representation, the more training examples are necessary to choose among the large number of "representablev possibilities.

      • Example of a representation
        • x1/x2 = # of black/red pieces on the board
        • x3/x4 = # of black/red king on the board
        • x5/x6 = # of black/red pieces threatened by red/black
        • V(b) = w0+w1.x1+w2.x2+w3.x3+w4.x4+w5.x5+w6.x6
                     Note that: wi's are adjustable or "learnable" coefficients

      5. Choosing a Machine Learning Algorithms Cost Function Approximation

      • Generating Training Examples of the form <b,Vtrain(b)>
        For example, <x1=3, x2=0, x3=1, x4=0, x5=0, x6=0, +100 (=blacks won):
        • Useful and Easy Approach: Vtrain(b) <- V(Successor(b))
      • Training the System
        • Defining a criterion for success - What is the error that needs to be minimized?
        • Choose an algorithm capable of finding weights of a linear function that minimize that error - For example, the Least Mean Square (LMS) training rule.

      6. Machine Learning Final Exam Solution Design

      • The Performance Module - Takes as input a new board and outputs a trace of the game it played against itself.
      • The Critic - Takes as input the trace of a game and outputs a set of training examples of the target function.
      • The Generalizer - Takes as input training examples and outputs a hypothesis which estimates the target function. Good generalization to new cases is crucial.
      • The Experiment Generator - Takes as input the current hypothesis (currently learned function) and outputs a new problem (an initial board state) for the performance system to explore.

      Machine Learning Issues

      The issues in machine learning or open challenges or problems in machine learning can be summarized as follows:-

      • Are some training examples more useful than others?
      • Deep Reinforcement Learning
      • How do we determine which learning method is appropriate for what type of software development or maintenance task?
      • How much training data is sufficient to learn a concept with high confidence?
      • Multimodal Learning
      • Reasoning and Natural Language Understanding
      • Unsupervised Learning / One-Shot & Transfer Learning
      • What algorithms are available for learning a concept? How well do they perform?
      • What ar ethe characteristics and underpinnings of different learning algorithms?
      • What are best tasks for a system to learn?
      • What is the best way for a system to represent its knowledge?
      • What is the state-of-the-practice in machinelearning and software engineering?
      • What types of learning methods are there available at our disposal?
      • When is it useful to use prior knowledge?
      • When we attempt to use some learning method to help with an SE task, what are the general guidelines and how can we avoid some pitfalls?
      • Where is further effort needed to produced fruitfull results?
      • Which learning methods can be used to make headway in what apsect of the essential difficulties in software development for AI

      Deep Learning Example: Image Understanding

      Deep learning example

      Consider the above deep learning example: Image understanding of Professor Antonio Torralba of Massachusetts Institute of Technology (MIT) Computer Science and Artificial Intelligence Laboratory in Department of Electrical Engineering and Computer Science, USA.

      Professor Torralba is researching in the areas of computer vision, machine learning and human visual perception; and is interested in scene and object recognition, among other things.

      The above image photo was taken at the Network for Integrated Behavioural Science (NIBS) conference by Professor Ruslan Salakhutdinov.

      Deep Learning Example: What can you do with the image?
      You can TAGS the image as "strangers", "coworkers", "conventioneers" or "attendants".

      Deep learning system can described what is going on as:
      Nearest Neighbour sentence: people taking pictures of a crazy person.

      A Statistical models can describe the image with a Model Samples:

      • a group of people in a crowded area
      • a group of people are walking and talking
      • a group of people, standing around and talking

      Valid Deep Learning Examples: Caption Generation

      Deep learning example #2 Deep learning example #3 Deep learning example #4

      Invalid Deep Learning Examples: Caption Generation

      Deep learning example #5 Deep learning example #6 Deep learning example #7

      Multimodal Learning Strategies

      Find below a multimodal linguistic regularities or Multimodal learning strategies or deep learning tutorial for multimodal machine learning. Nearest images:-

      Deep learning tutorial #1

      Learning to Read Books

      Find below learn to read books or books for learning to read for which Recurrent Neural Network reads 11K books, 74 million sentences. No supervision, no labelled data!

      Learn to read books

      Neuroscience and Storytelling

      Find below Neuroscience and Storytelling or Neural story telling for which a fiction book title is given and Recurrent Neural Network brings sample detail of the book:-

      Neuroscience and storytelling
      She was in love with him for the first time in months, so she had no intention of escaping. The sun had risen from th eocean, making her feel more alive than normal. She is beatiful, but thetruth is that I do not know what to do. The sun was just starting to fad away, leaving people scattered around the Atlantic Ocean.

      Semantic Relatedness

      Find below Semantic Relatedness or Semantic relatedness measures for which Recurrent Neural Network is asked: "How similar the two sentences are on the scale 1 to 5?"

      Semantic relatedness

      One-Shot Learning or Transfer Learning

      Find below one-shot learning in neural networks or transfer learning for which we ask "How can we gets our systems to learn new concept or new thing??" Lots of deep learning models require lots and lots of examples to learn the pattern recognition. Can you find other images of "zarc"?

      One-shot learning or transfer learning     One-shot learning zarc     One-shot learning segway
      By definition, One-shot learning is an object categorization problem in computer vision. Despite recent breakthroughs in the applications of deep neural networks, one setting that presents a persistent challenge is that of One transfer or one shot learning.

      Traditional gradient-based networks require a lot of data to learn, often through extensive iterative training.

      Machine Learning Book

      Machine learning book or Books about machine learning have been written that do a superb job of covering its main principles.

      Below are good machine learning books or good books for machine learning that we recommended that you can purchase at Amazon.com or amazon.co.uk

      Here is an incredibly useful books and magazines onmachine learning - it's quick, it's easy and it gets results.

        MACHINE LEARNING BOOK TITLE "MACHINE LEARNING DESCRIPTION"
       A Compendium of Machine Learning Machine learning is a relatively new branch of artificial intelligence. The field has undergone a significant period of growth in the 1990s, with many new areas of research and development being explored.
       A First Course in Machine Learning A First Course in Machine Learning covers the core mathematical and statistical techniques needed to understand some of the most popular machine learning algorithms. The algorithms presented span the main problem areas within machine learning: classification, clustering and projection. The text gives detailed descriptions and derivations for a small number of algorithms rather than cover many algorithms in less detail. Referenced throughout the text and available on a supporting website (http://bit.ly/firstcourseml), an extensive collection of MATLAB/Octave scripts enables students to recreate plots that appear in the book and investigate changing model specifications and parameter values. By experimenting with the various algorithms and concepts, students see how an abstract set of equations can be used to solve real problems. Requiring minimal mathematical prerequisites, the classroom-tested material in this text offers a concise, accessible introduction to machine learning. It provides students with the knowledge and confidence to explore the machine learning literature and research specific methods in more detail.
       Advances in Machine Learning and Data Mining for Astronomy Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.
       Bayesian Reasoning and Machine Learning A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.
       C4.5 Classifier systems play a major role in machine learning and knowledge-based systems, and Ross Quinlan's work on ID3 and C4.5 is widely acknowledged to have made some of the most significant contributions to their development. This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use , the source code (about 8,800 lines), and implementation notes. The source code and sample datasets are also available for download (see below). C4.5 starts with large sets of cases belonging to known classes. The cases, described by any mixture of nominal and numeric properties, are scrutinized for patterns that allow the classes to be reliably discriminated. These patterns are then expressed as models, in the form of decision trees or sets of if-then rules, that can be used to classify new cases, with emphasis on making the models understandable as well as accurate. The system has been applied successfully to tasks involving tens of thousands of cases described by hundreds of properties. The book starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting. Advantages and disadvantages of the C4.5 approach are discussed and illustrated with several case studies. This book and software should be of interest to developers of classification-based intelligent systems and to students in machine learning and expert systems courses.
       Data Mining As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more. * Algorithmic methods at the heart of successful data mining-including tried and true techniques as well as leading edge methods * Performance improvement techniques that work by transforming the input or output * Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization-in a new, interactive interface.
       Density Ratio Estimation in Machine Learning This book introduces theories, methods and applications of density ratio estimation, a newly emerging paradigm in the machine learning community.
       Deterministic and Statistical Methods in Machine Learning A textbook suitable for undergraduate courses in machine learning and related topics, this book provides a broad survey of the field. Generous exercises and examples give students a firm grasp of the concepts and techniques of this rapidly developing, challenging subject. Introduction to Machine Learning synthesizes and clarifies the work of leading researchers, much of which is otherwise available only in undigested technical reports, journals, and conference proceedings. Beginning with an overview suitable for undergraduate readers, Kodratoff establishes a theoretical basis for machine learning and describes its technical concepts and major application areas. Relevant logic programming examples are given in Prolog. Introduction to Machine Learning is an accessible and original introduction to a significant research area.
       Elements of Machine Learning Machine learning is the computational study of algorithms that improve performance based on experience, and this book covers the basic issues of artificial intelligence. Individual sections introduce the basic concepts and problems in machine learning, describe algorithms, discuss adaptions of the learning methods to more complex problem-solving tasks and much more.
       Gaussian Processes for Machine Learning A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.
       Genetic Algorithms for Machine Learning The articles presented here were selected from preliminary versions presented at the International Conference on Genetic Algorithms in June 1991, as well as at a special Workshop on Genetic Algorithms for Machine Learning at the same Conference. Genetic algorithms are general-purpose search algorithms that use principles inspired by natural population genetics to evolve solutions to problems. The basic idea is to maintain a population of knowledge structure that represent candidate solutions to the problem of interest. The population evolves over time through a process of competition (i.e. survival of the fittest) and controlled variation (i.e. recombination and mutation). Genetic Algorithms for Machine Learning contains articles on three topics that have not been the focus of many previous articles on GAs, namely concept learning from examples, reinforcement learning for control, and theoretical analysis of GAs. It is hoped that this sample will serve to broaden the acquaintance of the general machine learning community with the major areas of work on GAs. The articles in this book address a number of central issues in applying GAs to machine learning problems. For example, the choice of appropriate representation and the corresponding set of genetic learning operators is an important set of decisions facing a user of a genetic algorithm. The study of genetic algorithms is proceeding at a robust pace. If experimental progress and theoretical understanding continue to evolve as expected, genetic algorithms will continue to provide a distinctive approach to machine learning. Genetic Algorithms for Machine Learning is an edited volume of original research made up of invited contributions by leading researchers.
       Graphical Models for Machine Learning and Digital Communication Includes bibliographical references and index.
       Introduction to Machine Learning The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.
       Introduction to Machine Learning with Python Many Python developers are curious about what machine learning is and how it can be concretely applied to solve issues faced in businesses handling medium to large amount of data. Machine Learning with Python teaches you the basics of machine learning and provides a thorough hands-on understanding of the subject. You'll learn important machine learning concepts and algorithms, when to use them, and how to use them. The book will cover a machine learning workflow: data preprocessing and working with data, training algorithms, evaluating results, and implementing those algorithms into a production-level system.
       Machine Learning Multistrategy learning is one of the newest and most promising research directions in the development of machine learning systems. The objectives of research in this area are to study trade-offs between different learning strategies and to develop learning systems that employ multiple types of inference or computational paradigms in a learning process. Multistrategy systems offer significant advantages over monostrategy systems. They are more flexible in the type of input they can learn from and the type of knowledge they can acquire. As a consequence, multistrategy systems have the potential to be applicable to a wide range of practical problems. This volume is the first book in this fast growing field. It contains a selection of contributions by leading researchers specializing in this area. See below for earlier volumes in the series.
       Machine Learning Applications in Software Engineering Machine learning deals with the issue of how to build computerprograms that improve their performance at some tasks throughexperience. Machine learning algorithms have proven to be of greatpractical value in a variety of application domains. Not surprisingly, the field of software engineering turns out to be a fertile groundwhere many software development and maintenance tasks could beformulated as learning problems and approached in terms of learningalgorithms
       Machine Learning Approaches to Bioinformatics This book covers a wide range of subjects in applying machine learning approaches for bioinformatics projects. The book succeeds on two key unique features. First, it introduces the most widely used machine learning approaches in bioinformatics and discusses, with evaluations from real case studies, how they are used in individual bioinformatics projects. Second, it introduces state-of-the-art bioinformatics research methods. The theoretical parts and the practical parts are well integrated for readers to follow the existing procedures in individual research. Unlike most of the bioinformatics books on the market, the content coverage is not limited to just one subject. A broad spectrum of relevant topics in bioinformatics including systematic data mining and computational systems biology researches are brought together in this book, thereby offering an efficient and convenient platform for teaching purposes. An essential reference for both final year undergraduates and graduate students in universities, as well as a comprehensive handbook for new researchers, this book will also serve as a practical guide for software development in relevant bioinformatics projects.
       Machine Learning Methods for Ecological Applications The final chapter reviews 'real learning', offering the potential for greater dialogue between the biological and machine learning communities.--Jacket.
       Machine Learning and Systems Engineering A large international conference on Advances in Machine Learning and Systems Engineering was held in UC Berkeley, California, USA, October 20-22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009). Machine Learning and Systems Engineering contains forty-six revised and extended research articles written by prominent researchers participating in the conference. Topics covered include Expert system, Intelligent decision making, Knowledge-based systems, Knowledge extraction, Data analysis tools, Computational biology, Optimization algorithms, Experiment designs, Complex system identification, Computational modeling, and industrial applications. Machine Learning and Systems Engineering offers the state of the art of tremendous advances in machine learning and systems engineering and also serves as an excellent reference text for researchers and graduate students, working on machine learning and systems engineering.
       Machine Learning for Audio, Image and Video Analysis This second edition focuses on audio, image and video data, the three main types of input that machines deal with when interacting with the real world. A set of appendices provides the reader with self-contained introductions to the mathematical background necessary to read the book. Divided into three main parts, From Perception to Computation introduces methodologies aimed at representing the data in forms suitable for computer processing, especially when it comes to audio and images. Whilst the second part, Machine Learning includes an extensive overview of statistical techniques aimed at addressing three main problems, namely classification (automatically assigning a data sample to one of the classes belonging to a predefined set), clustering (automatically grouping data samples according to the similarity of their properties) and sequence analysis (automatically mapping a sequence of observations into a sequence of human-understandable symbols). The third part Applications shows how the abstract problems defined in the second part underlie technologies capable to perform complex tasks such as the recognition of hand gestures or the transcription of handwritten data. Machine Learning for Audio, Image and Video Analysis is suitable for students to acquire a solid background in machine learning as well as for practitioners to deepen their knowledge of the state-of-the-art. All application chapters are based on publicly available data and free software packages, thus allowing readers to replicate the experiments.
       Machine Learning for Hackers Presents algorithms that enable computers to train themselves to automate tasks, focusing on specific problems such as prediction, optimization, and classification.
       Machine Learning in Action Provides information on the concepts of machine theory, covering such topics as statistical data processing, data visualization, and forecasting.
       Machine Learning in Computer Vision A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
       Machine Learning in Non-stationary Environments As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of-the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non-stationarity.
       Machine Learning with Spark If you are a Scala, Java, or Python developer with an interest in machine learning and data analysis and are eager to learn how to apply common machine learning techniques at scale using the Spark framework, this is the book for you. While it may be useful to have a basic understanding of Spark, no previous experience is required.
       Optimization for Machine Learning The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields.Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.
       Pattern Recognition and Machine Learning Recognition and learning by a computer. Representing information. Generation and transformation of representations. Pattern feature extraction. Pattern understanding methods. Learning concepts. Learning procedures. Learning based on logic. Learning by classification and discovery. Learning by neural networks.
       Python Machine Learning Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask - and answer - tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning - whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data - its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
       Readings in Machine Learning The ability to learn is a fundamental characteristic of intelligent behavior. Consequently, machine learning has been a focus of artificial intelligence since the beginnings of AI in the 1950s. The 1980s saw tremendous growth in the field, and this growth promises to continue with valuable contributions to science, engineering, and business. Readings in Machine Learning collects the best of the published machine learning literature, including papers that address a wide range of learning tasks, and that introduce a variety of techniques for giving machines the ability to learn. The editors, in cooperation with a group of expert referees, have chosen important papers that empirically study, theoretically analyze, or psychologically justify machine learning algorithms. The papers are grouped into a dozen categories, each of which is introduced by the editors.
       Reinforcement and Systemic Machine Learning for Decision Making Reinforcement and Systemic Machine Learning for Decision Making explores a newer and growing avenue of machine learning algorithm in the area of computational intelligence. This book focuses on reinforcement and systemic learning to build a new learning paradigm, which makes effective use of these learning methodologies to increase machine intelligence and help us in building the advance machine learning applications. Illuminating case studies reflecting the authors' industrial experiences and pragmatic downloadable tutorials are available for researchers and professionals.
       Sequential Methods in Pattern Recognition and Machine Learning Sequential Methods in Pattern Recognition and Machine Learning.
       The Computational Complexity of Machine Learning We also give algorithms for learning powerful concept classes under the uniform distribution, and give equivalences between natural models of efficient learnability. This thesis also includes detailed definitions and motivation for the distribution-free model, a chapter discussing past research in this model and related models, and a short list of important open problems.
       Understanding Machine Learning Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.