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<center><h1>Introduction to Applied Data Analysis and Machine Learning</h1></center> <!-- document title -->
<p>
<!-- author(s): Morten Hjorth-Jensen -->
<center>
<b>Morten Hjorth-Jensen</b> [1, 2]
</center>
<p>
<!-- institution(s) -->
<center>[1] <b>Department of Physics, University of Oslo</b></center>
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>May 28, 2018</h4></center> <!-- date -->
<br>
<h2 id="___sec0">Introduction </h2>
<p>
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.
<p>
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.
<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. Many of the methods we will study are also
strongly rooted in basic mathematics and physics research.
<p>
Ideally, 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. You should however always keep in mind that
machines and algorithms are to a large extent developed by humans. The
insights and knowledge we have about a specific system, play a central
role when we develop a specific machine learning algorithm.
<p>
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
<a href="http://scikit-learn.org/stable/" target="_blank">Scikit-learn</a>,
<a href="https://www.tensorflow.org/" target="_blank">Tensorflow</a>,
<a href="http://pytorch.org/" target="_blank">PyTorch</a> and <a href="https://keras.io/" target="_blank">Keras</a>, 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.
<p>
<!-- !split -->
<h2 id="___sec1">Learning outcomes </h2>
<p>
These setsof 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.
<p>
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
<ol>
<li> learn about basic data analysis, Bayesian statistics, Monte Carlo methods, data optimization and machine learning;</li>
<li> be capable of extending the acquired knowledge to other systems and cases;</li>
<li> Have an understanding of central algorithms used in data analysis and machine learning;</li>
<li> 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;</li>
<li> Understand methods for regression and classification;</li>
<li> Learn about neural network, genetic algorithms and Boltzmann machines;</li>
<li> 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).</li>
</ol>
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.
<p>
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.
<p>
The second part of the material covers several algorithms used in
machine learning.
<h2 id="___sec2">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
behavioral 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>
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 (which can be subdivided into
training and test data), the second item is a model which is normally a
function of some parameters. The model reflects our knowledge of the system (or lack thereof). As an example, if we know that our data show a behavior similar to what would be predicted by a polynomial, fitting our data to a polynomial of some degree would then determin our model.
<p>
The last ingredient is a so-called <b>cost</b>
function which allows us to present an estimate on how good our model
is in reproducing the data it is supposed to train.
<p>
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
<ol>
<li> Linear regression and its variants, in essence polynomial regression</li>
<li> Decision tree algorithms, from simpler to more complex ones</li>
<li> Nearest neighbors models</li>
<li> Bayesian statistics and regression</li>
<li> Support vector machines and finally various variants of</li>
<li> Artifical neural networks and deep learning</li>
<li> Networks for unsupervised learning using for example reduced Boltzmann machines.</li>
</ol>
<h2 id="___sec3">Choice of programming language </h2>
<p>
Python plays nowadays a central role in the development of machine
learning techniques and tools for data analysis. In particular, seen
the wealth of machine learning and data analysis packages written in
Python, easy to use libraries with immediate visualization(and not the
least impressive galleries of existing example), the popularity of the
Jupyter notebook framework with the possibility to run <b>R</b> codes or
compiled programs written in C++, and much more made our choice of
programming language for this series of lectures of easy. However,
since the focus here is not only on using existing Python tools such
as <b>scikit-learn</b> or <b>tensorflow</b>, but also on developing your own
algorithms and codes, we will as far as possible present many of these
algorithms eithers a Python codes or C++ codes. Finally, we will, as
far as possible keep parallel versions of the data analysis and
machine larning programming aspects in <b>R</b> as
well. <a href="https://www.r-project.org/" target="_blank">R</a> is a language and environment
for statistical computing and graphics which is widely used in
statistics and mathematics applications.
<p>
The reason we also focus on compiled languages like C++ (or
Fortran), is that Python is still notoriously slow when we do not
utilize highly streamlined computational libraries like
<a href="http://www.netlib.org/lapack/" target="_blank">Lapack</a> or other numerical libraries
written in compiled languages (many of these libraries are written in
Fortran). Although a project like <a href="https://numba.pydata.org/" target="_blank">Numba</a>
holds great promise for speeding up the unrolling of lengthy loops, C+
and Fortran are presently still the performance winners. Numba gives
you potentially the power to speed up your applications with high
performance functions written directly in Python. In particular,
array-oriented and math-heavy Python code can achieve similar
performance to C, C++ and Fortran. However, even with these speed-ups,
for codes involving heavy Markov Chain Monte Carlo analyses and
optimizations of cost functions, C++/C or Fortran codes tend to
outperform Python codes.
<p>
Presently thus, the community tends to let
code written in C++/C or Fortran do the heavy duty numerical
number crunching and leave the post-analysis of the data to the above
mentioned Python modules or software packages. However, with the developments taking place in for example the Python community, and seen
the changes during the last decade, the above situation may change swiftly in the not too distant future.
<p>
Many of the examples we discuss in this series of lectures come with
existing data files or provide code examples which produce the data to
be analyzed. Most of the applications we will discuss deal with
small data sets (less than a terabyte of information) and can easily
be analyzed and tested on standard off the shelf laptops you find in general
grocery stores.
<h2 id="___sec4">Data handling, machine learning and ethical aspects </h2>
<p>
In most of the cases we will study, we will either generate the data
to analyze ourselves (both for supervised learning and unsupervised
learning) or we will recur again and again to data present in say
<b>scikit-learn</b> or <b>tensorflow</b>. Many of the examples we end up
dealing with are from a privacy and data protection point of view,
rather inoccuous and boring results of numerical
calculations. However, this does not hinder us from developing a sound
ethical attitude to the data we use, how we analyze the data and how
we handle the data.
<p>
The most immediate and simplest possible ethical aspects deal with our
approach to the scientific process. Nowadays, with version control
software like <a href="https://git-scm.com/" target="_blank">Git</a> and various online
repositories like <a href="https://github.com/" target="_blank">Github</a>,
<a href="https://about.gitlab.com/" target="_blank">Gitlab</a> etc, we can easily make our codes
and data sets we have used, freely and easily accessible to a wider
community. This helps us almost automagically in making our science
reproducible. The large open-source development communities involved
in say <a href="http://scikit-learn.org/stable/" target="_blank">Scikit-learn</a>,
<a href="https://www.tensorflow.org/" target="_blank">Tensorflow</a>,
<a href="http://pytorch.org/" target="_blank">PyTorch</a> and <a href="https://keras.io/" target="_blank">Keras</a>, are
all excellent examples of this. The codes can be tested and improved
upon continuosly, helping thereby our scientific community at large in
developing data analysis and machine learning tools. It is much
easier today to gain traction and acceptance for making your science
reproducible. From a societal stand, this is an important element
since many of the developers are employees of large public institutions like
universities and research labs. Our taxpayer do deserve to get
something back for their bucks.
<p>
However, this more mechanical aspect of the ethics of science (in
particular the reproducibility of scientific results) is something
which is obvious and everybody should do as part of the dialectics of
science. The fact that many scientists are not willing to share their codes or
data is detrimental to the scientific discourse.
<p>
Before we proceed, we should add a disclaimer. Even though
we may dream of computers developing some kind of higher learning
capabilities, at the end (even if the artificial intelligence
community keeps touting our ears full of fancy futuristic avenues), it is we
who end up constructing and instructing, via various algorithms, the
computers. Self-driving cars for example, rely on sofisticated
programs which take into account all possible situations a car can
encounter. In addition, extensive usage of training datas from GPS
information, maps etc, are typically fed into the software for
self-driving cars. Adding to this various sensors and cameras that
feed information to the programs, there are zillions of ethical issues
which arise from this.
<p>
For self-driving cars, where basically many of the standard machine
learning algorithms discussed here enter into the codes, at a certain
stage we have to make choices. Yes, we , the lads and lasses who wrote
a program for a specific brand of a self-driving car. As an example,
a most carmakers have as their utmost priority the security of the
driver and the accompanying passengers. A famous carmaker, which is
one of the leaders in the market of self-driving cars, had <b>if</b>
statements of the following type: suppose there are two obstacles in
front of you and you cannot avoid to collide with one of them. One of
the obstacles is a monstertruck while the other one is a kindergarten
class trying to cross the road. The self-driving car algo would then
opt for the hitting the small folks instead of the monstertruck, since
the likelihood of surving a collision with our future citizens, is
much higher.
<p>
This brings us leads then to serious ethical aspects. Why should we
opt for such an option? Who decides and who is entitled to make such
choices? Keep in mind that many of the algorithms you will about in
this series of lectures or hear about later, are indeed based on
simple programming instructions. And you are very likely to be one of
the people who may end up writing such a code. Thus, developing a
sound ethical attitude to what we do, an approach well beyond the
simple mechanistic one of making our science available and
reproducible, is much needed. The example of the self-driving cars is
just one of infinitely many cases where we have to make choices. When
you analyze data on economic inequalities, who guarantees that you are
not weighting some data in a particular way, perhaps because you dearly want a
specific conclusion which may support your political views?
<p>
We do not have the answers here, but we want you think over these
topics in a more overarching way. A statistical data analysis with
its dry numbers and graphs meant to guide the eye, do not necessarily
reflect the truth, whatever that is. As a scientist, and after a
university education, you are supposedly a better citizen, with an
improved critical view and understanding of the scientific method, and
perhaps some deeper understandings of the ethics of science at
large. Use these insights. Be a critical citizen. You owe it to our
societies.
<p>
To do: Add references and acknowledgements
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<!-- copyright --> &copy; 1999-2018, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
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