diff --git a/doc/pub/Introduction/html/Introduction-bs.html b/doc/pub/Introduction/html/Introduction-bs.html new file mode 100644 index 000000000..ec565f021 --- /dev/null +++ b/doc/pub/Introduction/html/Introduction-bs.html @@ -0,0 +1,327 @@ + + + + + + + +Data Analysis and Machine Learning: Representing data + + + + + + + + + + + + + + + + +
+
+ + Data Analysis and Machine Learning: Representing data +
+ +
+ +
+
+ + +
+ +

 

 

 

+ + + + + +
+

Data Analysis and Machine Learning: Representing data

+ +

+ + +

+Morten Hjorth-Jensen [1, 2] +
+ +

+ + +

[1] Department of Physics, University of Oslo
+
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
+
+

+

May 22, 2018

+
+

+ +

+ + + +

Introduction

+ +

+Statistics, data science and machine learning form important fields of +research in modern science. They describe how to learn and make +predictions from data, as well as allowing us to extract important +correlations about physical process and the underlying laws of motion +in large data sets. The latter, big data sets, appear +frequently in essentially all disciplines, from the traditional Science, +Technology, Mathematics and Engineering fields to Life Science, Law, education research, +the Humanities and +the Social Sciences. It has become more and more common to see +research projects on big data in for example the Social +Sciences where extracting patterns from complicated survey data is one of many research directions. +Having a solid grasp of data analysis and machine learning +is thus becoming central to scientific computing in many +fields, and competences and skills within the fields of machine learning +and scientific computing are nowadays strongly requested by many +potential employers. The latter cannot be overstated, familiarity with +machine learning has almost become a prerequisite for many of the most +exciting employment opportunities, whether they are in bioinformatics, +life science, physics or finance, in the private or the public +sector. This author has had several students or met students who have +been hired recently based on their skills and competences in +scientific computing and data science, often with marginal knowledge +of machine learning. + +

+Machine learning is a subfield of computer science, and is closely +related to computational statistics. It evolved from the study of +pattern recognition in artificial intelligence (AI) research, and has +made contributions to AI tasks like computer vision, natural language +processing and speech recognition. +Machine learning represents the +science of giving computers the ability to learn without being +explicitly programmed. The idea is that there exist generic +algorithms which can be used to find patterns in a broad class of data +sets without having to write code specifically for each problem. The +algorithm will build its own logic based on the data. + +

+Machine learning is an extremely rich field, in spite of its young age. The +increases we have seen during the last three decades in computational +capabilities have been followed by developments of methods and +techniques for analyzing and handling large date sets, relying heavily +on statistics, computer science and mathematics. The field is rather +new and developing rapidly. Popular software packages written in +Python for machine learning like Scikit-learn, Tensorflow, +PyTorch and Keras, all freely available at their respective GitHub sites, +encompass communities of developers in the thousands or more. And the number +of code developers and contributors keeps increasing. Not all the +algorithms and methods can be given a rigorous mathematical +justification, opening up thereby large rooms for experimenting +and trial and error and thereby exciting new developments. +However, a solid command of linear algebra, multivariate theory, +probability theory, statistical data analysis, +understanding errors and Monte Carlo methods are central elements in a proper understanding of many of +algorithms and methods we will discuss. + +

+ + +

Learning outcomes

+ +

+These lectures aim at giving you an overview of central aspects of +statistical data analysis as well as some of the central algorithms +used in machine learning. We will introduce a variety of central +algorithms and methods essential for studies of data analysis and +machine learning. + +

+Hands-on projects and experimenting with data and algorithms plays a central role in +these lectures, and our hope is, through the various +projects and exercies, to expose you to fundamental +research problems in these fields, with the aim to reproduce state of +the art scientific results. You will learn to develop and +structure large codes for studying these systems, get acquainted with +computing facilities and learn to handle large scientific projects. A +good scientific and ethical conduct is emphasized throughout the +course. More specifically, you will + +

    +
  1. learn about basic data analysis, Bayesian statistics, Monte Carlo methods, data optimization and machine learning;
    2. +
  2. be capable of extending the acquired knowledge to other systems and cases;
    3. +
  3. Have an understanding of central algorithms used in data analysis and machine learning;
    4. +
  4. Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers and their possible applications, from numerical integration to simulation of stock markets;
    5. +
  5. Understand methods for regression and classification;
    6. +
  6. Learn about neural network, genetic algorithms and Boltzmann machines;
    7. +
  7. Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++, in addition to a basic knowledge of linear algebra (typically taught during the first one or two years of undergraduate studies).
    8. +
+ +There are several topics we will cover here, spanning from a +statistical data analysis and its basic concepts such expectation +values, variance, covariance, correlation functions and errors, via +well-known probability distribution functions like uniform +distribution, the binomial distribution, the Poisson distribution and +simple and multivariate normal distributions to central elements of +Bayesian statistics and modeling. We will also remind the reader about +central elements from linear algebra and standard methods based on +linear algebra used to fit functions such Cubic splines and gradient +methods for data optimization and the Singular-value decomposition and +least square methods for parameterizing data. + +

+We will also cover Monte Carlo methods, Markov chains, well-known +algorithms for sampling stochastic events like the Metropolis-Hastings +and Gibbs sampling methods. An important aspect of all our +calculations is a proper estimation of errors. Here we will also +discuss famous resampling techniques like the blocking, bootstrapping +and jackknife methods. + +

+The second part of the material covers several algorithms used in +machine learning. + +

+ + +

Types of Machine Learning

+ +

+The approaches to machine learning are many, but are often split into two main categories. +In supervised learning we know the answer to a problem, +and let the computer deduce the logic behind it. On the other hand, unsupervised learning +is a method for finding patterns and relationship in data sets without any prior knowledge of the system. +Some authours also operate with a third category, namely reinforcement learning. This is a paradigm +of learning inspired by behavioral psychology, where learning is achieved by trial-and-error, +solely from rewards and punishment. + +

+Another way to categorize machine learning tasks is to consider the desired output of a system. +Some of the most common tasks are: + +

+ +The methods we cover have three main topics in common, irrespective of +whether we deal with supervised or unsupervised learning. The first +ingredient is normally our data set, the second is a model which is +normally a function of some parameters. The last ingredient is a +so-called cost function which allows us to present an estimate on +how good our model is in reproducing the data it is supposed to train. + +

+Here we will build our machine learning approach on elements of the +statistical foundation discussed above, with elements from data +analysis, stochastic processes etc. We will discuss the following +machine learning algorithms + +

    +
  1. Linear regression and its variants, in essence polynomial regression
    2. +
  2. Decision tree algorithms, from simpler to more complex ones
    3. +
  3. Nearest neighbors models
    4. +
  4. Bayesian statistics and regression
    5. +
  5. Support vector machines and finally various variants of
    6. +
  6. Artifical neural networks and deep learning
    7. +
+ + + +

Why this text?

+ +

+ + +

Choice of programming language

+ +

+ + +

Data handling, machine learning and ethical aspects

+ +

+ + +

Acknowledgements

+ +

+ + + +

+ + + + + + + +
+ © 1999-2018, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license +
+ + + + + + diff --git a/doc/pub/Introduction/html/Introduction-reveal.html b/doc/pub/Introduction/html/Introduction-reveal.html new file mode 100644 index 000000000..a34392da4 --- /dev/null +++ b/doc/pub/Introduction/html/Introduction-reveal.html @@ -0,0 +1,478 @@ +\ + + + + + + +Data Analysis and Machine Learning: Representing data + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + + + +
+ + + + +

Data Analysis and Machine Learning: Representing data

+ +

+ + +

+Morten Hjorth-Jensen [1, 2] +
+ +

 
+ + +

[1] Department of Physics, University of Oslo
+
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
+
+

 
+

May 22, 2018

+
+

+ +

+ © 1999-2018, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license +
+
+ + +
+

Introduction

+ +

+Statistics, data science and machine learning form important fields of +research in modern science. They describe how to learn and make +predictions from data, as well as allowing us to extract important +correlations about physical process and the underlying laws of motion +in large data sets. The latter, big data sets, appear +frequently in essentially all disciplines, from the traditional Science, +Technology, Mathematics and Engineering fields to Life Science, Law, education research, +the Humanities and +the Social Sciences. It has become more and more common to see +research projects on big data in for example the Social +Sciences where extracting patterns from complicated survey data is one of many research directions. +Having a solid grasp of data analysis and machine learning +is thus becoming central to scientific computing in many +fields, and competences and skills within the fields of machine learning +and scientific computing are nowadays strongly requested by many +potential employers. The latter cannot be overstated, familiarity with +machine learning has almost become a prerequisite for many of the most +exciting employment opportunities, whether they are in bioinformatics, +life science, physics or finance, in the private or the public +sector. This author has had several students or met students who have +been hired recently based on their skills and competences in +scientific computing and data science, often with marginal knowledge +of machine learning. + +

+Machine learning is a subfield of computer science, and is closely +related to computational statistics. It evolved from the study of +pattern recognition in artificial intelligence (AI) research, and has +made contributions to AI tasks like computer vision, natural language +processing and speech recognition. +Machine learning represents the +science of giving computers the ability to learn without being +explicitly programmed. The idea is that there exist generic +algorithms which can be used to find patterns in a broad class of data +sets without having to write code specifically for each problem. The +algorithm will build its own logic based on the data. + +

+Machine learning is an extremely rich field, in spite of its young age. The +increases we have seen during the last three decades in computational +capabilities have been followed by developments of methods and +techniques for analyzing and handling large date sets, relying heavily +on statistics, computer science and mathematics. The field is rather +new and developing rapidly. Popular software packages written in +Python for machine learning like Scikit-learn, Tensorflow, +PyTorch and Keras, all freely available at their respective GitHub sites, +encompass communities of developers in the thousands or more. And the number +of code developers and contributors keeps increasing. Not all the +algorithms and methods can be given a rigorous mathematical +justification, opening up thereby large rooms for experimenting +and trial and error and thereby exciting new developments. +However, a solid command of linear algebra, multivariate theory, +probability theory, statistical data analysis, +understanding errors and Monte Carlo methods are central elements in a proper understanding of many of +algorithms and methods we will discuss. +

+ + +
+

Learning outcomes

+ +

+These lectures aim at giving you an overview of central aspects of +statistical data analysis as well as some of the central algorithms +used in machine learning. We will introduce a variety of central +algorithms and methods essential for studies of data analysis and +machine learning. + +

+Hands-on projects and experimenting with data and algorithms plays a central role in +these lectures, and our hope is, through the various +projects and exercies, to expose you to fundamental +research problems in these fields, with the aim to reproduce state of +the art scientific results. You will learn to develop and +structure large codes for studying these systems, get acquainted with +computing facilities and learn to handle large scientific projects. A +good scientific and ethical conduct is emphasized throughout the +course. More specifically, you will + +

    +

  1. learn about basic data analysis, Bayesian statistics, Monte Carlo methods, data optimization and machine learning;
    2. +

  2. be capable of extending the acquired knowledge to other systems and cases;
    3. +

  3. Have an understanding of central algorithms used in data analysis and machine learning;
    4. +

  4. Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers and their possible applications, from numerical integration to simulation of stock markets;
    5. +

  5. Understand methods for regression and classification;
    6. +

  6. Learn about neural network, genetic algorithms and Boltzmann machines;
    7. +

  7. Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++, in addition to a basic knowledge of linear algebra (typically taught during the first one or two years of undergraduate studies).
    8. +
+

+ +There are several topics we will cover here, spanning from a +statistical data analysis and its basic concepts such expectation +values, variance, covariance, correlation functions and errors, via +well-known probability distribution functions like uniform +distribution, the binomial distribution, the Poisson distribution and +simple and multivariate normal distributions to central elements of +Bayesian statistics and modeling. We will also remind the reader about +central elements from linear algebra and standard methods based on +linear algebra used to fit functions such Cubic splines and gradient +methods for data optimization and the Singular-value decomposition and +least square methods for parameterizing data. + +

+We will also cover Monte Carlo methods, Markov chains, well-known +algorithms for sampling stochastic events like the Metropolis-Hastings +and Gibbs sampling methods. An important aspect of all our +calculations is a proper estimation of errors. Here we will also +discuss famous resampling techniques like the blocking, bootstrapping +and jackknife methods. + +

+The second part of the material covers several algorithms used in +machine learning. +

+ + +
+

Types of Machine Learning

+ +

+The approaches to machine learning are many, but are often split into two main categories. +In supervised learning we know the answer to a problem, +and let the computer deduce the logic behind it. On the other hand, unsupervised learning +is a method for finding patterns and relationship in data sets without any prior knowledge of the system. +Some authours also operate with a third category, namely reinforcement learning. This is a paradigm +of learning inspired by behavioral psychology, where learning is achieved by trial-and-error, +solely from rewards and punishment. + +

+Another way to categorize machine learning tasks is to consider the desired output of a system. +Some of the most common tasks are: + +

    + +

  • Classification: Outputs are divided into two or more classes. The goal is to produce a model that assigns inputs into one of these classes. An example is to identify digits based on pictures of hand-written ones. Classification is typically supervised learning.
    • + +

  • Regression: Finding a functional relationship between an input data set and a reference data set. The goal is to construct a function that maps input data to continuous output values.
    • + +

  • Clustering: Data are divided into groups with certain common traits, without knowing the different groups beforehand. It is thus a form of unsupervised learning.
    • +
+

+ +The methods we cover have three main topics in common, irrespective of +whether we deal with supervised or unsupervised learning. The first +ingredient is normally our data set, the second is a model which is +normally a function of some parameters. The last ingredient is a +so-called cost function which allows us to present an estimate on +how good our model is in reproducing the data it is supposed to train. + +

+Here we will build our machine learning approach on elements of the +statistical foundation discussed above, with elements from data +analysis, stochastic processes etc. We will discuss the following +machine learning algorithms + +

    +

  1. Linear regression and its variants, in essence polynomial regression
    2. +

  2. Decision tree algorithms, from simpler to more complex ones
    3. +

  3. Nearest neighbors models
    4. +

  4. Bayesian statistics and regression
    5. +

  5. Support vector machines and finally various variants of
    6. +

  6. Artifical neural networks and deep learning
    7. +
+
+ + +
+

Why this text?

+
+ + +
+

Choice of programming language

+
+ + +
+

Data handling, machine learning and ethical aspects

+
+ + +
+

Acknowledgements

+
+ + + +
+
+ + + + + + + + + + + + diff --git a/doc/pub/Introduction/html/Introduction-solarized.html b/doc/pub/Introduction/html/Introduction-solarized.html new file mode 100644 index 000000000..a8ed8a3ff --- /dev/null +++ b/doc/pub/Introduction/html/Introduction-solarized.html @@ -0,0 +1,271 @@ + + + + + + + +Data Analysis and Machine Learning: Representing data + + + + + + + + + + + + + + + + + + + + + +

Data Analysis and Machine Learning: Representing data

+ +

+ + +

+Morten Hjorth-Jensen [1, 2] +
+ +

+ + +

[1] Department of Physics, University of Oslo
+
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
+
+

+

May 22, 2018

+
+

+









+ +

Introduction

+ +

+Statistics, data science and machine learning form important fields of +research in modern science. They describe how to learn and make +predictions from data, as well as allowing us to extract important +correlations about physical process and the underlying laws of motion +in large data sets. The latter, big data sets, appear +frequently in essentially all disciplines, from the traditional Science, +Technology, Mathematics and Engineering fields to Life Science, Law, education research, +the Humanities and +the Social Sciences. It has become more and more common to see +research projects on big data in for example the Social +Sciences where extracting patterns from complicated survey data is one of many research directions. +Having a solid grasp of data analysis and machine learning +is thus becoming central to scientific computing in many +fields, and competences and skills within the fields of machine learning +and scientific computing are nowadays strongly requested by many +potential employers. The latter cannot be overstated, familiarity with +machine learning has almost become a prerequisite for many of the most +exciting employment opportunities, whether they are in bioinformatics, +life science, physics or finance, in the private or the public +sector. This author has had several students or met students who have +been hired recently based on their skills and competences in +scientific computing and data science, often with marginal knowledge +of machine learning. + +

+Machine learning is a subfield of computer science, and is closely +related to computational statistics. It evolved from the study of +pattern recognition in artificial intelligence (AI) research, and has +made contributions to AI tasks like computer vision, natural language +processing and speech recognition. +Machine learning represents the +science of giving computers the ability to learn without being +explicitly programmed. The idea is that there exist generic +algorithms which can be used to find patterns in a broad class of data +sets without having to write code specifically for each problem. The +algorithm will build its own logic based on the data. + +

+Machine learning is an extremely rich field, in spite of its young age. The +increases we have seen during the last three decades in computational +capabilities have been followed by developments of methods and +techniques for analyzing and handling large date sets, relying heavily +on statistics, computer science and mathematics. The field is rather +new and developing rapidly. Popular software packages written in +Python for machine learning like Scikit-learn, Tensorflow, +PyTorch and Keras, all freely available at their respective GitHub sites, +encompass communities of developers in the thousands or more. And the number +of code developers and contributors keeps increasing. Not all the +algorithms and methods can be given a rigorous mathematical +justification, opening up thereby large rooms for experimenting +and trial and error and thereby exciting new developments. +However, a solid command of linear algebra, multivariate theory, +probability theory, statistical data analysis, +understanding errors and Monte Carlo methods are central elements in a proper understanding of many of +algorithms and methods we will discuss. + +

+ + +

Learning outcomes

+ +

+These lectures aim at giving you an overview of central aspects of +statistical data analysis as well as some of the central algorithms +used in machine learning. We will introduce a variety of central +algorithms and methods essential for studies of data analysis and +machine learning. + +

+Hands-on projects and experimenting with data and algorithms plays a central role in +these lectures, and our hope is, through the various +projects and exercies, to expose you to fundamental +research problems in these fields, with the aim to reproduce state of +the art scientific results. You will learn to develop and +structure large codes for studying these systems, get acquainted with +computing facilities and learn to handle large scientific projects. A +good scientific and ethical conduct is emphasized throughout the +course. More specifically, you will + +

    +
  1. learn about basic data analysis, Bayesian statistics, Monte Carlo methods, data optimization and machine learning;
    2. +
  2. be capable of extending the acquired knowledge to other systems and cases;
    3. +
  3. Have an understanding of central algorithms used in data analysis and machine learning;
    4. +
  4. Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers and their possible applications, from numerical integration to simulation of stock markets;
    5. +
  5. Understand methods for regression and classification;
    6. +
  6. Learn about neural network, genetic algorithms and Boltzmann machines;
    7. +
  7. Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++, in addition to a basic knowledge of linear algebra (typically taught during the first one or two years of undergraduate studies).
    8. +
+ +There are several topics we will cover here, spanning from a +statistical data analysis and its basic concepts such expectation +values, variance, covariance, correlation functions and errors, via +well-known probability distribution functions like uniform +distribution, the binomial distribution, the Poisson distribution and +simple and multivariate normal distributions to central elements of +Bayesian statistics and modeling. We will also remind the reader about +central elements from linear algebra and standard methods based on +linear algebra used to fit functions such Cubic splines and gradient +methods for data optimization and the Singular-value decomposition and +least square methods for parameterizing data. + +

+We will also cover Monte Carlo methods, Markov chains, well-known +algorithms for sampling stochastic events like the Metropolis-Hastings +and Gibbs sampling methods. An important aspect of all our +calculations is a proper estimation of errors. Here we will also +discuss famous resampling techniques like the blocking, bootstrapping +and jackknife methods. + +

+The second part of the material covers several algorithms used in +machine learning. + +

+









+ +

Types of Machine Learning

+ +

+The approaches to machine learning are many, but are often split into two main categories. +In supervised learning we know the answer to a problem, +and let the computer deduce the logic behind it. On the other hand, unsupervised learning +is a method for finding patterns and relationship in data sets without any prior knowledge of the system. +Some authours also operate with a third category, namely reinforcement learning. This is a paradigm +of learning inspired by behavioral psychology, where learning is achieved by trial-and-error, +solely from rewards and punishment. + +

+Another way to categorize machine learning tasks is to consider the desired output of a system. +Some of the most common tasks are: + +

+ +The methods we cover have three main topics in common, irrespective of +whether we deal with supervised or unsupervised learning. The first +ingredient is normally our data set, the second is a model which is +normally a function of some parameters. The last ingredient is a +so-called cost function which allows us to present an estimate on +how good our model is in reproducing the data it is supposed to train. + +

+Here we will build our machine learning approach on elements of the +statistical foundation discussed above, with elements from data +analysis, stochastic processes etc. We will discuss the following +machine learning algorithms + +

    +
  1. Linear regression and its variants, in essence polynomial regression
    2. +
  2. Decision tree algorithms, from simpler to more complex ones
    3. +
  3. Nearest neighbors models
    4. +
  4. Bayesian statistics and regression
    5. +
  5. Support vector machines and finally various variants of
    6. +
  6. Artifical neural networks and deep learning
    7. +
+ +









+ +

Why this text?

+ +

+









+ +

Choice of programming language

+ +

+









+ +

Data handling, machine learning and ethical aspects

+ +

+









+ +

Acknowledgements

+ +

+ + + + +

+ © 1999-2018, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license +
+ + + + + + diff --git a/doc/pub/Introduction/html/Introduction.html b/doc/pub/Introduction/html/Introduction.html new file mode 100644 index 000000000..971aa236b --- /dev/null +++ b/doc/pub/Introduction/html/Introduction.html @@ -0,0 +1,276 @@ + + + + + + + +Data Analysis and Machine Learning: Representing data + + + + + + + + + + + + + + + + +

Data Analysis and Machine Learning: Representing data

+ +

+ + +

+Morten Hjorth-Jensen [1, 2] +
+ +

+ + +

[1] Department of Physics, University of Oslo
+
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
+
+

+

May 22, 2018

+
+

+









+ +

Introduction

+ +

+Statistics, data science and machine learning form important fields of +research in modern science. They describe how to learn and make +predictions from data, as well as allowing us to extract important +correlations about physical process and the underlying laws of motion +in large data sets. The latter, big data sets, appear +frequently in essentially all disciplines, from the traditional Science, +Technology, Mathematics and Engineering fields to Life Science, Law, education research, +the Humanities and +the Social Sciences. It has become more and more common to see +research projects on big data in for example the Social +Sciences where extracting patterns from complicated survey data is one of many research directions. +Having a solid grasp of data analysis and machine learning +is thus becoming central to scientific computing in many +fields, and competences and skills within the fields of machine learning +and scientific computing are nowadays strongly requested by many +potential employers. The latter cannot be overstated, familiarity with +machine learning has almost become a prerequisite for many of the most +exciting employment opportunities, whether they are in bioinformatics, +life science, physics or finance, in the private or the public +sector. This author has had several students or met students who have +been hired recently based on their skills and competences in +scientific computing and data science, often with marginal knowledge +of machine learning. + +

+Machine learning is a subfield of computer science, and is closely +related to computational statistics. It evolved from the study of +pattern recognition in artificial intelligence (AI) research, and has +made contributions to AI tasks like computer vision, natural language +processing and speech recognition. +Machine learning represents the +science of giving computers the ability to learn without being +explicitly programmed. The idea is that there exist generic +algorithms which can be used to find patterns in a broad class of data +sets without having to write code specifically for each problem. The +algorithm will build its own logic based on the data. + +

+Machine learning is an extremely rich field, in spite of its young age. The +increases we have seen during the last three decades in computational +capabilities have been followed by developments of methods and +techniques for analyzing and handling large date sets, relying heavily +on statistics, computer science and mathematics. The field is rather +new and developing rapidly. Popular software packages written in +Python for machine learning like Scikit-learn, Tensorflow, +PyTorch and Keras, all freely available at their respective GitHub sites, +encompass communities of developers in the thousands or more. And the number +of code developers and contributors keeps increasing. Not all the +algorithms and methods can be given a rigorous mathematical +justification, opening up thereby large rooms for experimenting +and trial and error and thereby exciting new developments. +However, a solid command of linear algebra, multivariate theory, +probability theory, statistical data analysis, +understanding errors and Monte Carlo methods are central elements in a proper understanding of many of +algorithms and methods we will discuss. + +

+ + +

Learning outcomes

+ +

+These lectures aim at giving you an overview of central aspects of +statistical data analysis as well as some of the central algorithms +used in machine learning. We will introduce a variety of central +algorithms and methods essential for studies of data analysis and +machine learning. + +

+Hands-on projects and experimenting with data and algorithms plays a central role in +these lectures, and our hope is, through the various +projects and exercies, to expose you to fundamental +research problems in these fields, with the aim to reproduce state of +the art scientific results. You will learn to develop and +structure large codes for studying these systems, get acquainted with +computing facilities and learn to handle large scientific projects. A +good scientific and ethical conduct is emphasized throughout the +course. More specifically, you will + +

    +
  1. learn about basic data analysis, Bayesian statistics, Monte Carlo methods, data optimization and machine learning;
    2. +
  2. be capable of extending the acquired knowledge to other systems and cases;
    3. +
  3. Have an understanding of central algorithms used in data analysis and machine learning;
    4. +
  4. Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers and their possible applications, from numerical integration to simulation of stock markets;
    5. +
  5. Understand methods for regression and classification;
    6. +
  6. Learn about neural network, genetic algorithms and Boltzmann machines;
    7. +
  7. Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++, in addition to a basic knowledge of linear algebra (typically taught during the first one or two years of undergraduate studies).
    8. +
+ +There are several topics we will cover here, spanning from a +statistical data analysis and its basic concepts such expectation +values, variance, covariance, correlation functions and errors, via +well-known probability distribution functions like uniform +distribution, the binomial distribution, the Poisson distribution and +simple and multivariate normal distributions to central elements of +Bayesian statistics and modeling. We will also remind the reader about +central elements from linear algebra and standard methods based on +linear algebra used to fit functions such Cubic splines and gradient +methods for data optimization and the Singular-value decomposition and +least square methods for parameterizing data. + +

+We will also cover Monte Carlo methods, Markov chains, well-known +algorithms for sampling stochastic events like the Metropolis-Hastings +and Gibbs sampling methods. An important aspect of all our +calculations is a proper estimation of errors. Here we will also +discuss famous resampling techniques like the blocking, bootstrapping +and jackknife methods. + +

+The second part of the material covers several algorithms used in +machine learning. + +

+









+ +

Types of Machine Learning

+ +

+The approaches to machine learning are many, but are often split into two main categories. +In supervised learning we know the answer to a problem, +and let the computer deduce the logic behind it. On the other hand, unsupervised learning +is a method for finding patterns and relationship in data sets without any prior knowledge of the system. +Some authours also operate with a third category, namely reinforcement learning. This is a paradigm +of learning inspired by behavioral psychology, where learning is achieved by trial-and-error, +solely from rewards and punishment. + +

+Another way to categorize machine learning tasks is to consider the desired output of a system. +Some of the most common tasks are: + +

+ +The methods we cover have three main topics in common, irrespective of +whether we deal with supervised or unsupervised learning. The first +ingredient is normally our data set, the second is a model which is +normally a function of some parameters. The last ingredient is a +so-called cost function which allows us to present an estimate on +how good our model is in reproducing the data it is supposed to train. + +

+Here we will build our machine learning approach on elements of the +statistical foundation discussed above, with elements from data +analysis, stochastic processes etc. We will discuss the following +machine learning algorithms + +

    +
  1. Linear regression and its variants, in essence polynomial regression
    2. +
  2. Decision tree algorithms, from simpler to more complex ones
    3. +
  3. Nearest neighbors models
    4. +
  4. Bayesian statistics and regression
    5. +
  5. Support vector machines and finally various variants of
    6. +
  6. Artifical neural networks and deep learning
    7. +
+ +









+ +

Why this text?

+ +

+









+ +

Choice of programming language

+ +

+









+ +

Data handling, machine learning and ethical aspects

+ +

+









+ +

Acknowledgements

+ +

+ + + + +

+ © 1999-2018, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license +
+ + + + + + diff --git a/doc/pub/Introduction/html/reveal.js/.gitignore b/doc/pub/Introduction/html/reveal.js/.gitignore new file mode 100644 index 000000000..e7b4f216a --- /dev/null +++ b/doc/pub/Introduction/html/reveal.js/.gitignore @@ -0,0 +1,13 @@ +.idea/ +*.iml +*.iws +*.eml +out/ +.DS_Store +.svn +log/*.log +tmp/** +node_modules/ +.sass-cache +css/reveal.min.css +js/reveal.min.js \ No newline at end of file diff --git a/doc/pub/Introduction/html/reveal.js/.travis.yml b/doc/pub/Introduction/html/reveal.js/.travis.yml new file mode 100644 index 000000000..ec3b27d5d --- /dev/null +++ b/doc/pub/Introduction/html/reveal.js/.travis.yml @@ -0,0 +1,7 @@ +language: node_js +node_js: + - 4 +before_script: + - npm install -g grunt-cli +after_script: + - grunt retire diff --git a/doc/pub/Introduction/html/reveal.js/CONTRIBUTING.md b/doc/pub/Introduction/html/reveal.js/CONTRIBUTING.md new file mode 100644 index 000000000..c2091e88f --- /dev/null +++ b/doc/pub/Introduction/html/reveal.js/CONTRIBUTING.md @@ -0,0 +1,23 @@ +## Contributing + +Please keep the [issue tracker](http://github.com/hakimel/reveal.js/issues) limited to **bug reports**, **feature requests** and **pull requests**. + + +### Personal Support +If you have personal support or setup questions the best place to ask those are [StackOverflow](http://stackoverflow.com/questions/tagged/reveal.js). + + +### Bug Reports +When reporting a bug make sure to include information about which browser and operating system you are on as well as the necessary steps to reproduce the issue. If possible please include a link to a sample presentation where the bug can be tested. + + +### Pull Requests +- Should follow the coding style of the file you work in, most importantly: + - Tabs to indent + - Single-quoted strings +- Should be made towards the **dev branch** +- Should be submitted from a feature/topic branch (not your master) + + +### Plugins +Please do not submit plugins as pull requests. They should be maintained in their own separate repository. More information here: https://github.com/hakimel/reveal.js/wiki/Plugin-Guidelines diff --git a/doc/pub/Introduction/html/reveal.js/Gruntfile.js b/doc/pub/Introduction/html/reveal.js/Gruntfile.js new file mode 100644 index 000000000..b257e8f32 --- /dev/null +++ b/doc/pub/Introduction/html/reveal.js/Gruntfile.js @@ -0,0 +1,140 @@ +/* global module:false */ +module.exports = function(grunt) { + var port = grunt.option('port') || 8000; + // Project configuration + grunt.initConfig({ + pkg: grunt.file.readJSON('package.json'), + meta: { + banner: + '/*!\n' + + ' * reveal.js <%= pkg.version %> (<%= grunt.template.today("yyyy-mm-dd, HH:MM") %>)\n' + + ' * http://lab.hakim.se/reveal-js\n' + + ' * MIT licensed\n' + + ' *\n' + + ' * Copyright (C) 2014 Hakim El Hattab, http://hakim.se\n' + + ' */' + }, + + qunit: { + files: [ 'test/*.html' ] + }, + + uglify: { + options: { + banner: '<%= meta.banner %>\n' + }, + build: { + src: 'js/reveal.js', + dest: 'js/reveal.min.js' + } + }, + + cssmin: { + compress: { + files: { + 'css/reveal.min.css': [ 'css/reveal.css' ] + } + } + }, + + sass: { + main: { + files: { + 'css/theme/darkgray.css': 'css/theme/source/darkgray.scss', + 'css/theme/beigesmall.css': 'css/theme/source/beigesmall.scss', + 'css/theme/cbc.css': 'css/theme/source/cbc.scss', + 'css/theme/default.css': 'css/theme/source/default.scss', + 'css/theme/beige.css': 'css/theme/source/beige.scss', + 'css/theme/night.css': 'css/theme/source/night.scss', + 'css/theme/serif.css': 'css/theme/source/serif.scss', + 'css/theme/simple.css': 'css/theme/source/simple.scss', + 'css/theme/sky.css': 'css/theme/source/sky.scss', + 'css/theme/moon.css': 'css/theme/source/moon.scss', + 'css/theme/solarized.css': 'css/theme/source/solarized.scss', + 'css/theme/blood.css': 'css/theme/source/blood.scss' + } + } + }, + + jshint: { + options: { + curly: false, + eqeqeq: true, + immed: true, + latedef: true, + newcap: true, + noarg: true, + sub: true, + undef: true, + eqnull: true, + browser: true, + expr: true, + globals: { + head: false, + module: false, + console: false, + unescape: false + } + }, + files: [ 'Gruntfile.js', 'js/reveal.js' ] + }, + + connect: { + server: { + options: { + port: port, + base: '.' + } + } + }, + + zip: { + 'reveal-js-presentation.zip': [ + 'index.html', + 'css/**', + 'js/**', + 'lib/**', + 'images/**', + 'plugin/**' + ] + }, + + watch: { + main: { + files: [ 'Gruntfile.js', 'js/reveal.js', 'css/reveal.css' ], + tasks: 'default' + }, + theme: { + files: [ 'css/theme/source/*.scss', 'css/theme/template/*.scss' ], + tasks: 'themes' + } + } + + }); + + // Dependencies + grunt.loadNpmTasks( 'grunt-contrib-qunit' ); + grunt.loadNpmTasks( 'grunt-contrib-jshint' ); + grunt.loadNpmTasks( 'grunt-contrib-cssmin' ); + grunt.loadNpmTasks( 'grunt-contrib-uglify' ); + grunt.loadNpmTasks( 'grunt-contrib-watch' ); + grunt.loadNpmTasks( 'grunt-contrib-sass' ); + grunt.loadNpmTasks( 'grunt-contrib-connect' ); + grunt.loadNpmTasks( 'grunt-zip' ); + + // Default task + grunt.registerTask( 'default', [ 'jshint', 'cssmin', 'uglify', 'qunit' ] ); + + // Theme task + grunt.registerTask( 'themes', [ 'sass' ] ); + + // Package presentation to archive + grunt.registerTask( 'package', [ 'default', 'zip' ] ); + + // Serve presentation locally + grunt.registerTask( 'serve', [ 'connect', 'watch' ] ); + + // Run tests + grunt.registerTask( 'test', [ 'jshint', 'qunit' ] ); + +}; diff --git a/doc/pub/Introduction/html/reveal.js/LICENSE b/doc/pub/Introduction/html/reveal.js/LICENSE new file mode 100644 index 000000000..c3e6e5fd6 --- /dev/null +++ b/doc/pub/Introduction/html/reveal.js/LICENSE @@ -0,0 +1,19 @@ +Copyright (C) 2017 Hakim El Hattab, http://hakim.se, and reveal.js contributors + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +THE SOFTWARE. \ No newline at end of file diff --git a/doc/pub/Introduction/html/reveal.js/README.md b/doc/pub/Introduction/html/reveal.js/README.md new file mode 100644 index 000000000..f2ab6ca88 --- /dev/null +++ b/doc/pub/Introduction/html/reveal.js/README.md @@ -0,0 +1,1246 @@ +# reveal.js [![Build Status](https://travis-ci.org/hakimel/reveal.js.svg?branch=master)](https://travis-ci.org/hakimel/reveal.js) Slides + +A framework for easily creating beautiful presentations using HTML. [Check out the live demo](http://revealjs.com/). + +reveal.js comes with a broad range of features including [nested slides](https://github.com/hakimel/reveal.js#markup), [Markdown contents](https://github.com/hakimel/reveal.js#markdown), [PDF export](https://github.com/hakimel/reveal.js#pdf-export), [speaker notes](https://github.com/hakimel/reveal.js#speaker-notes) and a [JavaScript API](https://github.com/hakimel/reveal.js#api). There's also a fully featured visual editor and platform for sharing reveal.js presentations at [slides.com](https://slides.com?ref=github). + +## Table of contents +- [Online Editor](#online-editor) +- [Instructions](#instructions) + - [Markup](#markup) + - [Markdown](#markdown) + - [Element Attributes](#element-attributes) + - [Slide Attributes](#slide-attributes) +- [Configuration](#configuration) +- [Presentation Size](#presentation-size) +- [Dependencies](#dependencies) +- [Ready Event](#ready-event) +- [Auto-sliding](#auto-sliding) +- [Keyboard Bindings](#keyboard-bindings) +- [Touch Navigation](#touch-navigation) +- [Lazy Loading](#lazy-loading) +- [API](#api) + - [Slide Changed Event](#slide-changed-event) + - [Presentation State](#presentation-state) + - [Slide States](#slide-states) + - [Slide Backgrounds](#slide-backgrounds) + - [Parallax Background](#parallax-background) + - [Slide Transitions](#slide-transitions) + - [Internal links](#internal-links) + - [Fragments](#fragments) + - [Fragment events](#fragment-events) + - [Code syntax highlighting](#code-syntax-highlighting) + - [Slide number](#slide-number) + - [Overview mode](#overview-mode) + - [Fullscreen mode](#fullscreen-mode) + - [Embedded media](#embedded-media) + - [Stretching elements](#stretching-elements) + - [postMessage API](#postmessage-api) +- [PDF Export](#pdf-export) +- [Theming](#theming) +- [Speaker Notes](#speaker-notes) + - [Share and Print Speaker Notes](#share-and-print-speaker-notes) + - [Server Side Speaker Notes](#server-side-speaker-notes) +- [Multiplexing](#multiplexing) + - [Master presentation](#master-presentation) + - [Client presentation](#client-presentation) + - [Socket.io server](#socketio-server) +- [MathJax](#mathjax) +- [Installation](#installation) + - [Basic setup](#basic-setup) + - [Full setup](#full-setup) + - [Folder Structure](#folder-structure) +- [License](#license) + +#### More reading +- [Changelog](https://github.com/hakimel/reveal.js/releases): Up-to-date version history. +- [Examples](https://github.com/hakimel/reveal.js/wiki/Example-Presentations): Presentations created with reveal.js, add your own! +- [Browser Support](https://github.com/hakimel/reveal.js/wiki/Browser-Support): Explanation of browser support and fallbacks. +- [Plugins](https://github.com/hakimel/reveal.js/wiki/Plugins,-Tools-and-Hardware): A list of plugins that can be used to extend reveal.js. + +## Online Editor + +Presentations are written using HTML or Markdown but there's also an online editor for those of you who prefer a graphical interface. Give it a try at [https://slides.com](https://slides.com?ref=github). + + +## Instructions + +### Markup + +Here's a barebones example of a fully working reveal.js presentation: +```html + + + + + + +
+
+
Slide 1
+
Slide 2
+
+
+ + + + +``` + +The presentation markup hierarchy needs to be `.reveal > .slides > section` where the `section` represents one slide and can be repeated indefinitely. If you place multiple `section` elements inside of another `section` they will be shown as vertical slides. The first of the vertical slides is the "root" of the others (at the top), and will be included in the horizontal sequence. For example: + +```html +
+
+
Single Horizontal Slide
+
+
Vertical Slide 1
+
Vertical Slide 2
+
+
+
+``` + +### Markdown + +It's possible to write your slides using Markdown. To enable Markdown, add the `data-markdown` attribute to your `
` elements and wrap the contents in a ` +
+``` + +#### External Markdown + +You can write your content as a separate file and have reveal.js load it at runtime. Note the separator arguments which determine how slides are delimited in the external file: the `data-separator` attribute defines a regular expression for horizontal slides (defaults to `^\r?\n---\r?\n$`, a newline-bounded horizontal rule) and `data-separator-vertical` defines vertical slides (disabled by default). The `data-separator-notes` attribute is a regular expression for specifying the beginning of the current slide's speaker notes (defaults to `note:`). The `data-charset` attribute is optional and specifies which charset to use when loading the external file. + +When used locally, this feature requires that reveal.js [runs from a local web server](#full-setup). The following example customises all available options: + +```html +
+ +
+``` + +#### Element Attributes + +Special syntax (in html comment) is available for adding attributes to Markdown elements. This is useful for fragments, amongst other things. + +```html +
+ +
+``` + +#### Slide Attributes + +Special syntax (in html comment) is available for adding attributes to the slide `
` elements generated by your Markdown. + +```html +
+ +
+``` + +#### Configuring *marked* + +We use [marked](https://github.com/chjj/marked) to parse Markdown. To customise marked's rendering, you can pass in options when [configuring Reveal](#configuration): + +```javascript +Reveal.initialize({ + // Options which are passed into marked + // See https://github.com/chjj/marked#options-1 + markdown: { + smartypants: true + } +}); +``` + +### Configuration + +At the end of your page you need to initialize reveal by running the following code. Note that all config values are optional and will default as specified below. + +```javascript +Reveal.initialize({ + + // Display presentation control arrows + controls: true, + + // Help the user learn the controls by providing hints, for example by + // bouncing the down arrow when they first encounter a vertical slide + controlsTutorial: true, + + // Determines where controls appear, "edges" or "bottom-right" + controlsLayout: 'bottom-right', + + // Visibility rule for backwards navigation arrows; "faded", "hidden" + // or "visible" + controlsBackArrows: 'faded', + + // Display a presentation progress bar + progress: true, + + // Set default timing of 2 minutes per slide + defaultTiming: 120, + + // Display the page number of the current slide + slideNumber: false, + + // Push each slide change to the browser history + history: false, + + // Enable keyboard shortcuts for navigation + keyboard: true, + + // Enable the slide overview mode + overview: true, + + // Vertical centering of slides + center: true, + + // Enables touch navigation on devices with touch input + touch: true, + + // Loop the presentation + loop: false, + + // Change the presentation direction to be RTL + rtl: false, + + // Randomizes the order of slides each time the presentation loads + shuffle: false, + + // Turns fragments on and off globally + fragments: true, + + // Flags if the presentation is running in an embedded mode, + // i.e. contained within a limited portion of the screen + embedded: false, + + // Flags if we should show a help overlay when the questionmark + // key is pressed + help: true, + + // Flags if speaker notes should be visible to all viewers + showNotes: false, + + // Global override for autoplaying embedded media (video/audio/iframe) + // - null: Media will only autoplay if data-autoplay is present + // - true: All media will autoplay, regardless of individual setting + // - false: No media will autoplay, regardless of individual setting + autoPlayMedia: null, + + // Number of milliseconds between automatically proceeding to the + // next slide, disabled when set to 0, this value can be overwritten + // by using a data-autoslide attribute on your slides + autoSlide: 0, + + // Stop auto-sliding after user input + autoSlideStoppable: true, + + // Use this method for navigation when auto-sliding + autoSlideMethod: Reveal.navigateNext, + + // Enable slide navigation via mouse wheel + mouseWheel: false, + + // Hides the address bar on mobile devices + hideAddressBar: true, + + // Opens links in an iframe preview overlay + // Add `data-preview-link` and `data-preview-link="false"` to customise each link + // individually + previewLinks: false, + + // Transition style + transition: 'slide', // none/fade/slide/convex/concave/zoom + + // Transition speed + transitionSpeed: 'default', // default/fast/slow + + // Transition style for full page slide backgrounds + backgroundTransition: 'fade', // none/fade/slide/convex/concave/zoom + + // Number of slides away from the current that are visible + viewDistance: 3, + + // Parallax background image + parallaxBackgroundImage: '', // e.g. "'https://s3.amazonaws.com/hakim-static/reveal-js/reveal-parallax-1.jpg'" + + // Parallax background size + parallaxBackgroundSize: '', // CSS syntax, e.g. "2100px 900px" + + // Number of pixels to move the parallax background per slide + // - Calculated automatically unless specified + // - Set to 0 to disable movement along an axis + parallaxBackgroundHorizontal: null, + parallaxBackgroundVertical: null, + + // The display mode that will be used to show slides + display: 'block' + +}); +``` + + +The configuration can be updated after initialization using the ```configure``` method: + +```javascript +// Turn autoSlide off +Reveal.configure({ autoSlide: 0 }); + +// Start auto-sliding every 5s +Reveal.configure({ autoSlide: 5000 }); +``` + + +### Presentation Size + +All presentations have a normal size, that is the resolution at which they are authored. The framework will automatically scale presentations uniformly based on this size to ensure that everything fits on any given display or viewport. + +See below for a list of configuration options related to sizing, including default values: + +```javascript +Reveal.initialize({ + + ... + + // The "normal" size of the presentation, aspect ratio will be preserved + // when the presentation is scaled to fit different resolutions. Can be + // specified using percentage units. + width: 960, + height: 700, + + // Factor of the display size that should remain empty around the content + margin: 0.1, + + // Bounds for smallest/largest possible scale to apply to content + minScale: 0.2, + maxScale: 1.5 + +}); +``` + +If you wish to disable this behavior and do your own scaling (e.g. using media queries), try these settings: + +```javascript +Reveal.initialize({ + + ... + + width: "100%", + height: "100%", + margin: 0, + minScale: 1, + maxScale: 1 +}); +``` + +### Dependencies + +Reveal.js doesn't _rely_ on any third party scripts to work but a few optional libraries are included by default. These libraries are loaded as dependencies in the order they appear, for example: + +```javascript +Reveal.initialize({ + dependencies: [ + // Cross-browser shim that fully implements classList - https://github.com/eligrey/classList.js/ + { src: 'lib/js/classList.js', condition: function() { return !document.body.classList; } }, + + // Interpret Markdown in
elements + { src: 'plugin/markdown/marked.js', condition: function() { return !!document.querySelector( '[data-markdown]' ); } }, + { src: 'plugin/markdown/markdown.js', condition: function() { return !!document.querySelector( '[data-markdown]' ); } }, + + // Syntax highlight for elements + { src: 'plugin/highlight/highlight.js', async: true, callback: function() { hljs.initHighlightingOnLoad(); } }, + + // Zoom in and out with Alt+click + { src: 'plugin/zoom-js/zoom.js', async: true }, + + // Speaker notes + { src: 'plugin/notes/notes.js', async: true }, + + // MathJax + { src: 'plugin/math/math.js', async: true } + ] +}); +``` + +You can add your own extensions using the same syntax. The following properties are available for each dependency object: +- **src**: Path to the script to load +- **async**: [optional] Flags if the script should load after reveal.js has started, defaults to false +- **callback**: [optional] Function to execute when the script has loaded +- **condition**: [optional] Function which must return true for the script to be loaded + +To load these dependencies, reveal.js requires [head.js](http://headjs.com/) *(a script loading library)* to be loaded before reveal.js. + +### Ready Event + +A 'ready' event is fired when reveal.js has loaded all non-async dependencies and is ready to start navigating. To check if reveal.js is already 'ready' you can call `Reveal.isReady()`. + +```javascript +Reveal.addEventListener( 'ready', function( event ) { + // event.currentSlide, event.indexh, event.indexv +} ); +``` + +Note that we also add a `.ready` class to the `.reveal` element so that you can hook into this with CSS. + +### Auto-sliding + +Presentations can be configured to progress through slides automatically, without any user input. To enable this you will need to tell the framework how many milliseconds it should wait between slides: + +```javascript +// Slide every five seconds +Reveal.configure({ + autoSlide: 5000 +}); +``` +When this is turned on a control element will appear that enables users to pause and resume auto-sliding. Alternatively, sliding can be paused or resumed by pressing »a« on the keyboard. Sliding is paused automatically as soon as the user starts navigating. You can disable these controls by specifying ```autoSlideStoppable: false``` in your reveal.js config. + +You can also override the slide duration for individual slides and fragments by using the ```data-autoslide``` attribute: + +```html +
+

After 2 seconds the first fragment will be shown.

+

After 10 seconds the next fragment will be shown.

+

Now, the fragment is displayed for 2 seconds before the next slide is shown.

+
+``` + +To override the method used for navigation when auto-sliding, you can specify the ```autoSlideMethod``` setting. To only navigate along the top layer and ignore vertical slides, set this to ```Reveal.navigateRight```. + +Whenever the auto-slide mode is resumed or paused the ```autoslideresumed``` and ```autoslidepaused``` events are fired. + + +### Keyboard Bindings + +If you're unhappy with any of the default keyboard bindings you can override them using the ```keyboard``` config option: + +```javascript +Reveal.configure({ + keyboard: { + 13: 'next', // go to the next slide when the ENTER key is pressed + 27: function() {}, // do something custom when ESC is pressed + 32: null // don't do anything when SPACE is pressed (i.e. disable a reveal.js default binding) + } +}); +``` + +### Touch Navigation + +You can swipe to navigate through a presentation on any touch-enabled device. Horizontal swipes change between horizontal slides, vertical swipes change between vertical slides. If you wish to disable this you can set the `touch` config option to false when initializing reveal.js. + +If there's some part of your content that needs to remain accessible to touch events you'll need to highlight this by adding a `data-prevent-swipe` attribute to the element. One common example where this is useful is elements that need to be scrolled. + + +### Lazy Loading + +When working on presentation with a lot of media or iframe content it's important to load lazily. Lazy loading means that reveal.js will only load content for the few slides nearest to the current slide. The number of slides that are preloaded is determined by the `viewDistance` configuration option. + +To enable lazy loading all you need to do is change your "src" attributes to "data-src" as shown below. This is supported for image, video, audio and iframe elements. Lazy loaded iframes will also unload when the containing slide is no longer visible. + +```html +
+ + + +
+``` + + +### API + +The ``Reveal`` object exposes a JavaScript API for controlling navigation and reading state: + +```javascript +// Navigation +Reveal.slide( indexh, indexv, indexf ); +Reveal.left(); +Reveal.right(); +Reveal.up(); +Reveal.down(); +Reveal.prev(); +Reveal.next(); +Reveal.prevFragment(); +Reveal.nextFragment(); + +// Randomize the order of slides +Reveal.shuffle(); + +// Toggle presentation states, optionally pass true/false to force on/off +Reveal.toggleOverview(); +Reveal.togglePause(); +Reveal.toggleAutoSlide(); + +// Shows a help overlay with keyboard shortcuts, optionally pass true/false +// to force on/off +Reveal.toggleHelp(); + +// Change a config value at runtime +Reveal.configure({ controls: true }); + +// Returns the present configuration options +Reveal.getConfig(); + +// Fetch the current scale of the presentation +Reveal.getScale(); + +// Retrieves the previous and current slide elements +Reveal.getPreviousSlide(); +Reveal.getCurrentSlide(); + +Reveal.getIndices(); // { h: 0, v: 0 } } +Reveal.getPastSlideCount(); +Reveal.getProgress(); // (0 == first slide, 1 == last slide) +Reveal.getSlides(); // Array of all slides +Reveal.getTotalSlides(); // total number of slides + +// Returns the speaker notes for the current slide +Reveal.getSlideNotes(); + +// State checks +Reveal.isFirstSlide(); +Reveal.isLastSlide(); +Reveal.isOverview(); +Reveal.isPaused(); +Reveal.isAutoSliding(); +``` + +### Slide Changed Event + +A 'slidechanged' event is fired each time the slide is changed (regardless of state). The event object holds the index values of the current slide as well as a reference to the previous and current slide HTML nodes. + +Some libraries, like MathJax (see [#226](https://github.com/hakimel/reveal.js/issues/226#issuecomment-10261609)), get confused by the transforms and display states of slides. Often times, this can be fixed by calling their update or render function from this callback. + +```javascript +Reveal.addEventListener( 'slidechanged', function( event ) { + // event.previousSlide, event.currentSlide, event.indexh, event.indexv +} ); +``` + +### Presentation State + +The presentation's current state can be fetched by using the `getState` method. A state object contains all of the information required to put the presentation back as it was when `getState` was first called. Sort of like a snapshot. It's a simple object that can easily be stringified and persisted or sent over the wire. + +```javascript +Reveal.slide( 1 ); +// we're on slide 1 + +var state = Reveal.getState(); + +Reveal.slide( 3 ); +// we're on slide 3 + +Reveal.setState( state ); +// we're back on slide 1 +``` + +### Slide States + +If you set ``data-state="somestate"`` on a slide ``
``, "somestate" will be applied as a class on the document element when that slide is opened. This allows you to apply broad style changes to the page based on the active slide. + +Furthermore you can also listen to these changes in state via JavaScript: + +```javascript +Reveal.addEventListener( 'somestate', function() { + // TODO: Sprinkle magic +}, false ); +``` + +### Slide Backgrounds + +Slides are contained within a limited portion of the screen by default to allow them to fit any display and scale uniformly. You can apply full page backgrounds outside of the slide area by adding a ```data-background``` attribute to your ```
``` elements. Four different types of backgrounds are supported: color, image, video and iframe. + +#### Color Backgrounds +All CSS color formats are supported, like rgba() or hsl(). +```html +
+

Color

+
+``` + +#### Image Backgrounds +By default, background images are resized to cover the full page. Available options: + +| Attribute | Default | Description | +| :--------------------------- | :--------- | :---------- | +| data-background-image | | URL of the image to show. GIFs restart when the slide opens. | +| data-background-size | cover | See [background-size](https://developer.mozilla.org/docs/Web/CSS/background-size) on MDN. | +| data-background-position | center | See [background-position](https://developer.mozilla.org/docs/Web/CSS/background-position) on MDN. | +| data-background-repeat | no-repeat | See [background-repeat](https://developer.mozilla.org/docs/Web/CSS/background-repeat) on MDN. | +```html +
+

Image

+
+
+

This background image will be sized to 100px and repeated

+
+``` + +#### Video Backgrounds +Automatically plays a full size video behind the slide. + +| Attribute | Default | Description | +| :--------------------------- | :------ | :---------- | +| data-background-video | | A single video source, or a comma separated list of video sources. | +| data-background-video-loop | false | Flags if the video should play repeatedly. | +| data-background-video-muted | false | Flags if the audio should be muted. | +| data-background-size | cover | Use `cover` for full screen and some cropping or `contain` for letterboxing. | + +```html +
+

Video

+
+``` + +#### Iframe Backgrounds +Embeds a web page as a slide background that covers 100% of the reveal.js width and height. The iframe is in the background layer, behind your slides, and as such it's not possible to interact with it by default. To make your background interactive, you can add the `data-background-interactive` attribute. +```html +
+

Iframe

+
+``` + +#### Background Transitions +Backgrounds transition using a fade animation by default. This can be changed to a linear sliding transition by passing ```backgroundTransition: 'slide'``` to the ```Reveal.initialize()``` call. Alternatively you can set ```data-background-transition``` on any section with a background to override that specific transition. + + +### Parallax Background + +If you want to use a parallax scrolling background, set the first two config properties below when initializing reveal.js (the other two are optional). + +```javascript +Reveal.initialize({ + + // Parallax background image + parallaxBackgroundImage: '', // e.g. "https://s3.amazonaws.com/hakim-static/reveal-js/reveal-parallax-1.jpg" + + // Parallax background size + parallaxBackgroundSize: '', // CSS syntax, e.g. "2100px 900px" - currently only pixels are supported (don't use % or auto) + + // Number of pixels to move the parallax background per slide + // - Calculated automatically unless specified + // - Set to 0 to disable movement along an axis + parallaxBackgroundHorizontal: 200, + parallaxBackgroundVertical: 50 + +}); +``` + +Make sure that the background size is much bigger than screen size to allow for some scrolling. [View example](http://revealjs.com/?parallaxBackgroundImage=https%3A%2F%2Fs3.amazonaws.com%2Fhakim-static%2Freveal-js%2Freveal-parallax-1.jpg¶llaxBackgroundSize=2100px%20900px). + + + +### Slide Transitions +The global presentation transition is set using the ```transition``` config value. You can override the global transition for a specific slide by using the ```data-transition``` attribute: + +```html +
+

This slide will override the presentation transition and zoom!

+
+ +
+

Choose from three transition speeds: default, fast or slow!

+
+``` + +You can also use different in and out transitions for the same slide: + +```html +
+ The train goes on … +
+
+ and on … +
+
+ and stops. +
+
+ (Passengers entering and leaving) +
+
+ And it starts again. +
+``` + + +### Internal links + +It's easy to link between slides. The first example below targets the index of another slide whereas the second targets a slide with an ID attribute (```
```): + +```html +Link +Link +``` + +You can also add relative navigation links, similar to the built in reveal.js controls, by appending one of the following classes on any element. Note that each element is automatically given an ```enabled``` class when it's a valid navigation route based on the current slide. + +```html + + + + + + +``` + + +### Fragments +Fragments are used to highlight individual elements on a slide. Every element with the class ```fragment``` will be stepped through before moving on to the next slide. Here's an example: http://revealjs.com/#/fragments + +The default fragment style is to start out invisible and fade in. This style can be changed by appending a different class to the fragment: + +```html +
+

grow

+

shrink

+

fade-out

+

fade-up (also down, left and right!)

+

visible only once

+

blue only once

+

highlight-red

+

highlight-green

+

highlight-blue

+
+``` + +Multiple fragments can be applied to the same element sequentially by wrapping it, this will fade in the text on the first step and fade it back out on the second. + +```html +
+ + I'll fade in, then out + +
+``` + +The display order of fragments can be controlled using the ```data-fragment-index``` attribute. + +```html +
+

Appears last

+

Appears first

+

Appears second

+
+``` + +### Fragment events + +When a slide fragment is either shown or hidden reveal.js will dispatch an event. + +Some libraries, like MathJax (see #505), get confused by the initially hidden fragment elements. Often times this can be fixed by calling their update or render function from this callback. + +```javascript +Reveal.addEventListener( 'fragmentshown', function( event ) { + // event.fragment = the fragment DOM element +} ); +Reveal.addEventListener( 'fragmenthidden', function( event ) { + // event.fragment = the fragment DOM element +} ); +``` + +### Code syntax highlighting + +By default, Reveal is configured with [highlight.js](https://highlightjs.org/) for code syntax highlighting. To enable syntax highlighting, you'll have to load the highlight plugin ([plugin/highlight/highlight.js](plugin/highlight/highlight.js)) and a highlight.js CSS theme (Reveal comes packaged with the zenburn theme: [lib/css/zenburn.css](lib/css/zenburn.css)). + +Below is an example with clojure code that will be syntax highlighted. When the `data-trim` attribute is present, surrounding whitespace is automatically removed. HTML will be escaped by default. To avoid this, for example if you are using `` to call out a line of code, add the `data-noescape` attribute to the `` element. + +```html +
+ 

+(def lazy-fib
+  (concat
+   [0 1]
+   ((fn rfib [a b]
+        (lazy-cons (+ a b) (rfib b (+ a b)))) 0 1)))
+
+
+``` + +### Slide number +If you would like to display the page number of the current slide you can do so using the ```slideNumber``` and ```showSlideNumber``` configuration values. + +```javascript +// Shows the slide number using default formatting +Reveal.configure({ slideNumber: true }); + +// Slide number formatting can be configured using these variables: +// "h.v": horizontal . vertical slide number (default) +// "h/v": horizontal / vertical slide number +// "c": flattened slide number +// "c/t": flattened slide number / total slides +Reveal.configure({ slideNumber: 'c/t' }); + +// Control which views the slide number displays on using the "showSlideNumber" value: +// "all": show on all views (default) +// "speaker": only show slide numbers on speaker notes view +// "print": only show slide numbers when printing to PDF +Reveal.configure({ showSlideNumber: 'speaker' }); + +``` + + +### Overview mode + +Press "Esc" or "o" keys to toggle the overview mode on and off. While you're in this mode, you can still navigate between slides, +as if you were at 1,000 feet above your presentation. The overview mode comes with a few API hooks: + +```javascript +Reveal.addEventListener( 'overviewshown', function( event ) { /* ... */ } ); +Reveal.addEventListener( 'overviewhidden', function( event ) { /* ... */ } ); + +// Toggle the overview mode programmatically +Reveal.toggleOverview(); +``` + + +### Fullscreen mode +Just press »F« on your keyboard to show your presentation in fullscreen mode. Press the »ESC« key to exit fullscreen mode. + + +### Embedded media +Add `data-autoplay` to your media element if you want it to automatically start playing when the slide is shown: + +```html + +``` + +If you want to enable or disable autoplay globally, for all embedded media, you can use the `autoPlayMedia` configuration option. If you set this to `true` ALL media will autoplay regardless of individual `data-autoplay` attributes. If you initialize with `autoPlayMedia: false` NO media will autoplay. + +Note that embedded HTML5 `