lars/FYS-STK4155
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

added introduction chapter

Browse Source
This commit is contained in:
mhjensen
2018-05-21 10:59:37 -04:00
parent 6437af32ef
commit 06c18466e5
55 changed files with 4077 additions and 2764 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/src/How2ReadData/How2ReadData.do.txt
+2 -151
View File
@@ -1,4 +1,4 @@
TITLE: Data Analysis and Machine Learning: Introduction and Representing data
TITLE: Data Analysis and Machine Learning: Getting started, our first data and Machine Learning encounters
AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
DATE: today
@@ -6,156 +6,7 @@ DATE: today
!split
===== 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 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":"http://scikit-learn.org/stable/", "Tensorflow":"https://www.tensorflow.org/",
"PyTorch":"http://pytorch.org/" and "Keras":"https://keras.io/", 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.
!split
===== 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
o learn about basic data analysis, Bayesian statistics, Monte Carlo methods, data optimization and machine learning;
o be capable of extending the acquired knowledge to other systems and cases;
o Have an understanding of central algorithms used in data analysis and machine learning;
o 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;
o Understand methods for regression and classification;
o Learn about neural network, genetic algorithms and Boltzmann machines;
o 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).
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.
!split
===== 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
o Linear regression and its variants, in essence polynomial regression
o Decision tree algorithms, from simpler to more complex ones
o Nearest neighbors models
o Bayesian statistics and regression
o Support vector machines and finally various variants of
o Artifical neural networks and deep learning
Before we proceed however, there are several practicalities with data
Before we proceed 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.