Update on machine learning

2017-11-27 22:22:51 +00:00
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-<meta name="description" content="Data Analysis and Machine Learning: Representing data">
+<meta name="description" content="Data Analysis and Machine Learning: Introduction and Representing data">

-<title>Data Analysis and Machine Learning: Representing data</title>
+<title>Data Analysis and Machine Learning: Introduction and Representing data</title>


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 <!-- tocinfo
 {'highest level': 2,
- 'sections': [('Representing data, overarching aims', 2, None, '___sec0'),
-              ('Representing data, overarching aims', 2, None, '___sec1')]}
+ 'sections': [('What is Machine Learning?', 2, None, '___sec0'),
+              ('Types of Machine Learning', 2, None, '___sec1'),
+              ('Different algorithms', 2, None, '___sec2'),
+              ('Software and needed installations', 2, None, '___sec3'),
+              ('Python installers', 2, None, '___sec4'),
+              ('Installing R and C++', 2, None, '___sec5'),
+              ('Introduction to Jupyter notebook and available tools',
+               2,
+               None,
+               '___sec6'),
+              ('Representing data, overarching aims', 2, None, '___sec7'),
+              ('Representing data, more examples', 2, None, '___sec8')]}
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-<center><h1>Data Analysis and Machine Learning: Representing data</h1></center>  <!-- document title -->
+<center><h1>Data Analysis and Machine Learning: Introduction and Representing data</h1></center>  <!-- document title -->

 <p>
 <!-- author(s): Morten Hjorth-Jensen -->
@@ -92,12 +102,151 @@ end of tocinfo -->
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Nov 26, 2017</h4></center> <!-- date -->
+<center><h4>Nov 27, 2017</h4></center> <!-- date -->
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 <!-- !split --><br><br><br><br><br><br><br><br><br><br>

-<h2 id="___sec0">Representing data, overarching aims  </h2>
+<h2 id="___sec0">What is Machine Learning? </h2>
+
+<p>
+Machine learning is 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.
+
+<p>
+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. It has also, especially in later
+years, found applications in a wide variety of other areas, including
+bioinformatics, economy, physics, finance and marketing.
+
+<p>
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+
+<h2 id="___sec1">Types of Machine Learning </h2>
+
+<p>
+The approaches to machine learning are many, but are often split into two main categories. 
+In <em>supervised learning</em> we know the answer to a problem,
+and let the computer deduce the logic behind it. On the other hand, <em>unsupervised learning</em>
+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 <em>reinforcement learning</em>. This is a paradigm 
+of learning inspired by behavioural psychology, where learning is achieved by trial-and-error, 
+solely from rewards and punishment.
+
+<p>
+Another way to categorize machine learning tasks is to consider the desired output of a system.
+Some of the most common tasks are:
+
+<ul>
+  <li> 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.</li>
+  <li> 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.</li>
+  <li> Clustering: Data are divided into groups with certain common traits, without knowing the different groups beforehand.  It is thus a form of unsupervised learning.</li>
+</ul>
+
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+
+<h2 id="___sec2">Different algorithms </h2>
+In this course we will build our machine learning approach on a statistical foundation, with elements 
+from data analysis, stochastic processes etc  before we proceed with the following machine learning algorithms
+
+<ol>
+<li> Linear regression and its variants</li>
+<li> Decision tree algorithms, from simpler to more complex ones</li>
+<li> Nearest neighbors models</li>
+<li> Bayesian statistics</li> 
+<li> Support vector machines and finally various variants of</li>
+<li> Artifical neural networks</li>
+</ol>
+
+Before we proceed however, there are several practicalities with data analysis and software tools we would 
+like to present. These tools will help us in our understanding of various machine learning algorithms.
+
+<p>
+Our emphasis here is on understanding the mathematical aspects of different algorithms, however, where possible 
+we will emphasize the importance of using available software.
+
+<p>
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+
+<h2 id="___sec3">Software and needed installations </h2>
+We will make intensive use of python as programming language and the myriad of available libraries. 
+Furthermore, you will find IPython/Jupyter notebooks invaluable in your work. 
+You can run <b>R</b> codes in the Jupyter/IPython notebooks, with the immediate benefit of visualizing your data.
+
+<p>
+If you have Python installed (we recommend Python3) and you feel pretty familiar with installing different packages, 
+we recommend that you install the following Python packages via <b>pip</b> as
+
+<ol>
+<li> pip install numpy scipy matplotlib ipython scikit-learn mglearn sympy pandas pillow</li>
+</ol>
+
+For Python3, replace <b>pip</b> with <b>pip3</b>.
+
+<p>
+For OSX user we recommend also, after having installed Xcode, to install <b>brew</b>. Brew allows 
+for a seamless installation of additional software via for example
+
+<ol>
+<li> brew install python3</li>
+</ol>
+
+For Linux users, with its variety of distributions like for example the widely popular Ubuntu distribution
+you can use <b>pip</b> as well and simply install Python as 
+
+<ol>
+<li> sudo apt-get install python3  (or python for pyhton2.7)</li>
+</ol>
+
+etc etc.
+
+<p>
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+
+<h2 id="___sec4">Python installers </h2>
+If you don't want to perform these operations separately, we recommend two widely used distrubutions which set up 
+all relevant dependencies for Python, namely
+
+<ol>
+<li> anaconda</li>
+<li> Enthought canopy</li>
+</ol>
+
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+
+<h2 id="___sec5">Installing R and C++ </h2>
+
+<p>
+You will also find it convenient to utilize R. Say more about R. 
+Jupyter/Ipython notebook allows you run <b>R</b> code interactively in your browser. The software library <b>R</b> is 
+tuned to statistically analysis and allows for an easy usage of the tools we will discuss in these texts.
+
+<p>
+For the C++ affecianodas, Jupyter/IPython notebook allows you also to install C++ and run codes written in this language 
+interactively in the browser. Since we will emphasize writing many of the algorithms yourself, you can thus opt for
+either Python or C++ as programming languages.
+
+<p>
+To add more entropy, <b>cython</b> can also be used when running your notebooks. It means that Python with the Jupyter/IPython notebook 
+setup allows you to integrate widely popular softwares and tools for scientific computing. With its versatility, 
+including symbolic operations, Python offers a unique computational environment. Your Jupyter/IPython notebook 
+can easily be converted into a nicely rendered <b>PDF</b> file or a Latex file for further processing.
+
+<p>
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+
+<h2 id="___sec6">Introduction to Jupyter notebook and available tools </h2>
+
+<p>
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+
+<h2 id="___sec7">Representing data, overarching aims  </h2>
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