mhjensen
495 lines
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HTML
495 lines
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<section>
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<!-- ------------------- main content ---------------------- -->
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<center><h1 style="text-align: center;">Data Analysis and Machine Learning: Introduction and Representing data</h1></center> <!-- document title -->
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<p>
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<!-- author(s): Morten Hjorth-Jensen -->
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<center>
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<b>Morten Hjorth-Jensen</b> [1, 2]
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</center>
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<p> <br>
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<!-- institution(s) -->
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<center>[1] <b>Department of Physics, University of Oslo</b></center>
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<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
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<br>
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<p> <br>
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<center><h4>Nov 27, 2017</h4></center> <!-- date -->
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<br>
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<p>
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<center style="font-size:80%">
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<!-- copyright --> © 1999-2017, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
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</center>
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</section>
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<section>
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<h2 id="___sec0">What is Machine Learning? </h2>
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<p>
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Machine learning is the science of giving computers the ability to
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learn without being explicitly programmed. The idea is that there
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exist generic algorithms which can be used to find patterns in a broad
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class of data sets without having to write code specifically for each
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problem. The algorithm will build its own logic based on the data.
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<p>
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Machine learning is a subfield of computer science, and is closely
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related to computational statistics. It evolved from the study of
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pattern recognition in artificial intelligence (AI) research, and has
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made contributions to AI tasks like computer vision, natural language
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processing and speech recognition. It has also, especially in later
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years, found applications in a wide variety of other areas, including
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bioinformatics, economy, physics, finance and marketing.
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</section>
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<section>
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<h2 id="___sec1">Types of Machine Learning </h2>
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<p>
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The approaches to machine learning are many, but are often split into two main categories.
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In <em>supervised learning</em> we know the answer to a problem,
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and let the computer deduce the logic behind it. On the other hand, <em>unsupervised learning</em>
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is a method for finding patterns and relationship in data sets without any prior knowledge of the system.
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Some authours also operate with a third category, namely <em>reinforcement learning</em>. This is a paradigm
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of learning inspired by behavioural psychology, where learning is achieved by trial-and-error,
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solely from rewards and punishment.
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<p>
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Another way to categorize machine learning tasks is to consider the desired output of a system.
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Some of the most common tasks are:
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<ul>
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<p><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>
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<p><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>
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<p><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>
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</ul>
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</section>
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<section>
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<h2 id="___sec2">Different algorithms </h2>
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In this course we will build our machine learning approach on a statistical foundation, with elements
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from data analysis, stochastic processes etc before we proceed with the following machine learning algorithms
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<ol>
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<p><li> Linear regression and its variants</li>
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<p><li> Decision tree algorithms, from simpler to more complex ones</li>
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<p><li> Nearest neighbors models</li>
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<p><li> Bayesian statistics</li>
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<p><li> Support vector machines and finally various variants of</li>
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<p><li> Artifical neural networks</li>
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</ol>
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<p>
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Before we proceed however, there are several practicalities with data analysis and software tools we would
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like to present. These tools will help us in our understanding of various machine learning algorithms.
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<p>
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Our emphasis here is on understanding the mathematical aspects of different algorithms, however, where possible
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we will emphasize the importance of using available software.
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</section>
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<section>
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<h2 id="___sec3">Software and needed installations </h2>
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We will make intensive use of python as programming language and the myriad of available libraries.
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Furthermore, you will find IPython/Jupyter notebooks invaluable in your work.
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You can run <b>R</b> codes in the Jupyter/IPython notebooks, with the immediate benefit of visualizing your data.
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<p>
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If you have Python installed (we recommend Python3) and you feel pretty familiar with installing different packages,
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we recommend that you install the following Python packages via <b>pip</b> as
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<ol>
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<p><li> pip install numpy scipy matplotlib ipython scikit-learn mglearn sympy pandas pillow</li>
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</ol>
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<p>
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For Python3, replace <b>pip</b> with <b>pip3</b>.
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<p>
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For OSX user we recommend also, after having installed Xcode, to install <b>brew</b>. Brew allows
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for a seamless installation of additional software via for example
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<ol>
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<p><li> brew install python3</li>
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</ol>
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<p>
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For Linux users, with its variety of distributions like for example the widely popular Ubuntu distribution
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you can use <b>pip</b> as well and simply install Python as
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<ol>
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<p><li> sudo apt-get install python3 (or python for pyhton2.7)</li>
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</ol>
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<p>
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etc etc.
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</section>
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<section>
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<h2 id="___sec4">Python installers </h2>
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If you don't want to perform these operations separately, we recommend two widely used distrubutions which set up
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all relevant dependencies for Python, namely
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<ol>
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<p><li> anaconda</li>
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<p><li> Enthought canopy</li>
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</ol>
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</section>
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<section>
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<h2 id="___sec5">Installing R and C++ </h2>
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<p>
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You will also find it convenient to utilize R. Say more about R.
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Jupyter/Ipython notebook allows you run <b>R</b> code interactively in your browser. The software library <b>R</b> is
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tuned to statistically analysis and allows for an easy usage of the tools we will discuss in these texts.
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<p>
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For the C++ affecianodas, Jupyter/IPython notebook allows you also to install C++ and run codes written in this language
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interactively in the browser. Since we will emphasize writing many of the algorithms yourself, you can thus opt for
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either Python or C++ as programming languages.
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<p>
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To add more entropy, <b>cython</b> can also be used when running your notebooks. It means that Python with the Jupyter/IPython notebook
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setup allows you to integrate widely popular softwares and tools for scientific computing. With its versatility,
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including symbolic operations, Python offers a unique computational environment. Your Jupyter/IPython notebook
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can easily be converted into a nicely rendered <b>PDF</b> file or a Latex file for further processing.
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</section>
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<section>
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<h2 id="___sec6">Introduction to Jupyter notebook and available tools </h2>
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</section>
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<section>
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<h2 id="___sec7">Representing data, overarching aims </h2>
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<div class="alert alert-block alert-block alert-text-normal">
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<b></b>
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<p>
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<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
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<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">np</span>
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<span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib.pyplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">scipy</span> <span style="color: #8B008B; font-weight: bold">import</span> sparse
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<span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">pandas</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">pd</span>
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">IPython.display</span> <span style="color: #8B008B; font-weight: bold">import</span> display
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eye = np.eye(<span style="color: #B452CD">4</span>)
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<span style="color: #8B008B; font-weight: bold">print</span>(eye)
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sparse_mtx = sparse.csr_matrix(eye)
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<span style="color: #8B008B; font-weight: bold">print</span>(sparse_mtx)
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x = np.linspace(-<span style="color: #B452CD">10</span>,<span style="color: #B452CD">10</span>,<span style="color: #B452CD">100</span>)
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y = np.sin(x)
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plt.plot(x,y,marker=<span style="color: #CD5555">'x'</span>)
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plt.show()
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data = {<span style="color: #CD5555">'Name'</span>: [<span style="color: #CD5555">"John"</span>, <span style="color: #CD5555">"Anna"</span>, <span style="color: #CD5555">"Peter"</span>, <span style="color: #CD5555">"Linda"</span>], <span style="color: #CD5555">'Location'</span>: [<span style="color: #CD5555">"Roma"</span>, <span style="color: #CD5555">"Napoli"</span>, <span style="color: #CD5555">"Torino"</span>, <span style="color: #CD5555">"Milano"</span>], <span style="color: #CD5555">'Age'</span>:[<span style="color: #B452CD">51</span>, <span style="color: #B452CD">21</span>, <span style="color: #B452CD">34</span>, <span style="color: #B452CD">45</span>]}
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data_pandas = pd.DataFrame(data)
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display(data_pandas)
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</pre></div>
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</div>
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</section>
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<section>
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<h2 id="___sec8">Representing data, more examples </h2>
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<div class="alert alert-block alert-block alert-text-normal">
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<b></b>
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<p>
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<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
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<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">np</span>
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<span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib.pyplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">scipy</span> <span style="color: #8B008B; font-weight: bold">import</span> sparse
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<span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">pandas</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">pd</span>
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">IPython.display</span> <span style="color: #8B008B; font-weight: bold">import</span> display
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<span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">mglearn</span>
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<span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">sklearn</span>
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.linear_model</span> <span style="color: #8B008B; font-weight: bold">import</span> LinearRegression
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.tree</span> <span style="color: #8B008B; font-weight: bold">import</span> DecisionTreeRegressor
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x, y = mglearn.datasets.make_wave(n_samples=<span style="color: #B452CD">100</span>)
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line = np.linspace(-<span style="color: #B452CD">3</span>,<span style="color: #B452CD">3</span>,<span style="color: #B452CD">1000</span>,endpoint=<span style="color: #658b00">False</span>).reshape(-<span style="color: #B452CD">1</span>,<span style="color: #B452CD">1</span>)
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reg = DecisionTreeRegressor(min_samples_split=<span style="color: #B452CD">3</span>).fit(x,y)
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plt.plot(line, reg.predict(line), label=<span style="color: #CD5555">"decision tree"</span>)
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regline = LinearRegression().fit(x,y)
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plt.plot(line, regline.predict(line), label= <span style="color: #CD5555">"Linear Rgression"</span>)
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plt.show()
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</pre></div>
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</div>
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</section>
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// Change the presentation direction to be RTL
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rtl: false,
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// Turns fragments on and off globally
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fragments: true,
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// Flags if the presentation is running in an embedded mode,
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// i.e. contained within a limited portion of the screen
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embedded: false,
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// Number of milliseconds between automatically proceeding to the
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// next slide, disabled when set to 0, this value can be overwritten
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// by using a data-autoslide attribute on your slides
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autoSlide: 0,
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// Stop auto-sliding after user input
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autoSlideStoppable: true,
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|
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// Enable slide navigation via mouse wheel
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mouseWheel: false,
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// Hides the address bar on mobile devices
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hideAddressBar: true,
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// Opens links in an iframe preview overlay
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previewLinks: false,
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|
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// Transition style
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transition: 'default', // default/cube/page/concave/zoom/linear/fade/none
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// Transition speed
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transitionSpeed: 'default', // default/fast/slow
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// Transition style for full page slide backgrounds
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backgroundTransition: 'default', // default/none/slide/concave/convex/zoom
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|
|
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// Number of slides away from the current that are visible
|
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viewDistance: 3,
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|
|
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// Parallax background image
|
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//parallaxBackgroundImage: '', // e.g. "'https://s3.amazonaws.com/hakim-static/reveal-js/reveal-parallax-1.jpg'"
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|
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// Parallax background size
|
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//parallaxBackgroundSize: '' // CSS syntax, e.g. "2100px 900px"
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|
|
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theme: Reveal.getQueryHash().theme, // available themes are in reveal.js/css/theme
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transition: Reveal.getQueryHash().transition || 'default', // default/cube/page/concave/zoom/linear/none
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|
|
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});
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|
|
|
Reveal.initialize({
|
|
dependencies: [
|
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// Cross-browser shim that fully implements classList - https://github.com/eligrey/classList.js/
|
|
{ src: 'reveal.js/lib/js/classList.js', condition: function() { return !document.body.classList; } },
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|
|
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// Interpret Markdown in <section> elements
|
|
{ src: 'reveal.js/plugin/markdown/marked.js', condition: function() { return !!document.querySelector( '[data-markdown]' ); } },
|
|
{ src: 'reveal.js/plugin/markdown/markdown.js', condition: function() { return !!document.querySelector( '[data-markdown]' ); } },
|
|
|
|
// Syntax highlight for <code> elements
|
|
{ src: 'reveal.js/plugin/highlight/highlight.js', async: true, callback: function() { hljs.initHighlightingOnLoad(); } },
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|
|
|
// Zoom in and out with Alt+click
|
|
{ src: 'reveal.js/plugin/zoom-js/zoom.js', async: true, condition: function() { return !!document.body.classList; } },
|
|
|
|
// Speaker notes
|
|
{ src: 'reveal.js/plugin/notes/notes.js', async: true, condition: function() { return !!document.body.classList; } },
|
|
|
|
// Remote control your reveal.js presentation using a touch device
|
|
//{ src: 'reveal.js/plugin/remotes/remotes.js', async: true, condition: function() { return !!document.body.classList; } },
|
|
|
|
// MathJax
|
|
//{ src: 'reveal.js/plugin/math/math.js', async: true }
|
|
]
|
|
});
|
|
|
|
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: 1170, // original: 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: 1.0
|
|
|
|
});
|
|
</script>
|
|
|
|
<!-- begin footer logo
|
|
<div style="position: absolute; bottom: 0px; left: 0; margin-left: 0px">
|
|
<img src="somelogo.png">
|
|
</div>
|
|
end footer logo -->
|
|
|
|
|
|
|
|
</body>
|
|
</html>
|