This site contains all material relevant for the course on Applied
Data Analysis and Machine Learning.
Introduction
Probability theory and statistical methods play a central role in Science. Nowadays we are
surrounded by huge amounts of data. For example, there are more than one trillion web pages; more than one
hour of video is uploaded to YouTube every second, amounting to years of content every
day; the genomes of 1000s of people, each of which has a length of more than a billion base pairs, have
been sequenced by various labs and so on. This deluge of data calls for automated methods of data analysis,
which is exactly what machine learning aims at providing.
Learning outcomes
This course aims at giving you insights and knowledge about many of the central algorithms used in Data Analysis and Machine Learning. The course is project based and through various numerical projects and weekly exercises you will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. Both supervised and unsupervised methods will be covered. The emphasis is on a frequentist approach with an emphasis on predictions and correaltions. However, we will try, where appropriate, to link our machine learning models with a Bayesian approach as well. You will learn to develop and structure large codes for studying different cases where Machine Learning is applied to, 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, after this course you will
Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning;
Be capable of extending the acquired knowledge to other systems and cases;
Have an understanding of central algorithms used in data analysis and machine learning;
Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression and Kernel regression;
Learn about neural networks and deep learning methods for supervised and unsupervised learning. Emphasis on feed forward neural networks, convolutional and recurrent neural networks;
Learn about about decision trees, random forests, bagging and boosting methods;
Learn about support vector machines and kernel transformations;
Reduction of data sets and unsupervised learning, from PCA to clustering;
Autoencoders and Reinforcement Learning;
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++ and/or Fortran (Fortran2003 or later).
Prerequisites and background
Basic knowledge in programming and mathematics, with an emphasis on linear algebra. Knowledge of Python or/and C++ as programming languages is strongly recommended and experience with Jupyter notebooks is recommended. Required courses are the equivalents to the University of Oslo mathematics courses MAT1100, MAT1110, MAT1120 and at least one of the corresponding computing and programming courses INF1000/INF1110 or MAT-INF1100/MAT-INF1100L/BIOS1100/KJM-INF1100. Most universities offer nowadays a basic programming course (often compulsory) where Python is the recurring programming language.
We recommend also refreshing your knowledge on Statistics and Probability theory. The lecture notes at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html offer a review of Statistics and Probability theory.
The course has two central parts
Statistical analysis and optimization of data
Machine learning
Statistical analysis and optimization of data
The following topics will be covered
Basic concepts, expectation values, variance, covariance, correlation functions and errors;
Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;
Central elements of Bayesian statistics and modeling;
Gradient methods for data optimization,
Monte Carlo methods, Markov chains, Gibbs sampling and Metropolis-Hastings sampling;
Estimation of errors and resampling techniques such as the cross-validation, blocking, bootstrapping and jackknife methods;
Principal Component Analysis (PCA) and its mathematical foundation
Machine learning
The following topics will be covered:
Linear Regression and Logistic Regression;
Neural networks and deep learning, including convolutional and recurrent neural networks
Decisions trees, Random Forests, Bagging and Boosting
Support vector machines
Bayesian linear and logistic regression
Boltzmann Machines
Unsupervised learning Dimensionality reduction, PCA, k-means and clustering
Autoenconders
Hands-on demonstrations, exercises and projects aim at deepening your understanding of these topics.
Computational aspects play a central role and you are
expected to work on numerical examples and projects which illustrate
the theory and various algorithms discussed during the lectures. We recommend strongly to form small project groups of 2-3 participants, if possible.
Office: Department of Physics, University of Oslo, Eastern wing, room FØ470
Office hours: Anytime! In Fall Semester 2021 we hope to be able to meet in person. Individual or group office hours can be performed either in person or via zoom. Feel free to send an email for planning. In-person meetings may also be possible if allowed by the University of Oslo's COVID-19 instructions (see below for links).
Four lectures per week, Fall semester, 10 ECTS. The lectures will be recorded and linked to this site and the official University of Oslo website for the course;
Two hours of laboratory sessions for work on computational projects and exercises for each group. Due to social distancing, at most 15 participants can attend. There will also be fully digital laboratory sessions for those who cannot attend;
Three projects which are graded and count 1/3 each of the final grade;
A selected number of weekly assignments;
The course is part of the CS Master of Science program, but is open to other bachelor and Master of Science students at the University of Oslo;
The course is offered as a so-called cloned course, FYS-STK4155 at the Master of Science level and FYS-STK3155 as a senior undergraduate)course;
Weekly email with summary of activities will be mailed to all participants;
Grading
Grading scale: Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. There are three projects which are graded and each project counts 1/3 of the final grade. The total score is thus the average from all three projects.
The final number of points is based on the average of all projects (including eventual additional points) and the grade follows the following table:
92-100 points: A
77-91 points: B
58-76 points: C
46-57 points: D
40-45 points: E
0-39 points: F-failed
Required Technologies
Course participants are expected to have their own laptops/PCs. We use Git as version control software and the usage of providers like GitHub, GitLab or similar are strongly recommended. If you are not familiar with Git as version control software, the following video may be of interest, see https://www.youtube.com/watch?v=RGOj5yH7evk&ab_channel=freeCodeCamp.org
We will make extensive use of Python as programming language and its
myriad of available libraries. You will find
Jupyter notebooks invaluable in your work. You can run R
codes in the Jupyter/IPython notebooks, with the immediate benefit of
visualizing your data. You can also use compiled languages like C++,
Rust, Julia, Fortran etc if you prefer. The focus in these lectures will be
on Python.
If you have Python installed and you feel
pretty familiar with installing different packages, we recommend that
you install the following Python packages via pip as
For OSX users we recommend, after having installed Xcode, to
install brew. Brew allows for a seamless installation of additional
software via for example
brew install python3
For Linux users, with its variety of distributions like for example the widely popular Ubuntu distribution,
you can use pip as well and simply install Python as
sudo apt-get install python3
Python installers
If you don't want to perform these operations separately and venture
into the hassle of exploring how to set up dependencies and paths, we
recommend two widely used distrubutions which set up all relevant
dependencies for Python, namely
which is an open source
distribution of the Python and R programming languages for large-scale
data processing, predictive analytics, and scientific computing, that
aims to simplify package management and deployment. Package versions
are managed by the package management system conda.
is a Python
distribution for scientific and analytic computing distribution and
analysis environment, available for free and under a commercial
license.
Here we list several useful Python libraries we strongly recommend (if you use anaconda many of these are already there)
NumPy:https://www.numpy.org/ is a highly popular library for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays
The pandas:https://pandas.pydata.org/ library provides high-performance, easy-to-use data structures and data analysis tools
Xarray:http://xarray.pydata.org/en/stable/ is a Python package that makes working with labelled multi-dimensional arrays simple, efficient, and fun!
Scipy:https://www.scipy.org/ (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering.
Matplotlib:https://matplotlib.org/ is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.
Autograd:https://github.com/HIPS/autograd can automatically differentiate native Python and Numpy code. It can handle a large subset of Python's features, including loops, ifs, recursion and closures, and it can even take derivatives of derivatives of derivatives
Recommended textbooks:
The lecture notes are collected as a jupyter-book at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html. In addition to the electure notes, we recommend the books of Bishop and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two texts.
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at https://www.deeplearningbook.org/. Chapters 2-14 are highly recommended. The lectures follow to a good extent this text.
The weekly plans will include reading suggestions from these two textbooks. In addition, you may find the following textbooks interesting.
Additional textbooks:
Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer, https://www.springer.com/gp/book/9780387848570. This is a well-known text and serves as additional literature.
IN5400/INF5860 Machine Learning for Image Analysishttps://www.uio.no/studier/emner/matnat/ifi/IN5400/. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.
TEK5040 Deep learning for autonomous systemshttps://www.uio.no/studier/emner/matnat/its/TEK5040/. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.
This course will be delivered in a hybrid mode, with online lectures and on site or online laboratory sessions.
Four lectures per week, Fall semester, 10 ECTS. The lectures are in person but will be recorded and linked to this site and the official University of Oslo website for the course;
Two hours of laboratory sessions for work on computational projects and exercises for each group. There will also be fully digital laboratory sessions for those who cannot attend;
Three projects which are graded and count 1/3 each of the final grade;
A selected number of weekly assignments;
The course is part of the CS Master of Science program, but is open to other bachelor and Master of Science students at the University of Oslo;
The course is offered as a FYS-MAT4155 (Master of Science level) and a FYS-MAT3155 (senior undergraduate) course;
Weekly emails with summary of activities will be mailed to all participants;
Communication channels
Chat and communications via canvas.uio.no, GDPR safe
Slack channel: machinelearninguio.slack.com
Piazza : enlist at https:piazza.com/uio.no/fall2021/fysstk4155
Weekly Schedule
For the reading assignments we use the following abbreviations:
GBC: Goodfellow, Bengio, and Courville, Deep Learning
CMB: Christopher M. Bishop, Pattern Recognition and Machine Learning
HTF: Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning
AG: Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow
Recommended prereading: Chapters 1-2 (linear algebra) and chapter 3 (statistics) of Goodfellow et al. and Bishop chapter 1 and chapter 2. These chapters give a relevant background to the basic mathematical and statistical foundations of the course. Parts of these chapters will be covered during the lectures the first three weeks.
Week 34 August 23-27
Lab Wednesday: Introduction to software and repetition of Python Programming
Lecture Thursday: Introduction to the course, what is Machine Learning and introduction to Linear Regression.
Bishop 4.1, 4.2 and 4.3. Not all the material is relevant or will be covered. Section 4.3 is the most relevant, but 4.1 and 4.2 give interesting background readings for logistic regression
Hastie et al 4.1, 4.2 and 4.3 on logistic regression
For a good discussion on gradient methods, see Goodfellow et al section 4.3-4.5 and chapter 8. We will come back to the latter chapter in our discussion of Neural networks as well.
For a good discussion on gradient methods, see Goodfellow et al section 4.3-4.5 and chapter 8. We will come back to the latter chapter in our discussion of Neural networks as well.
Week 40 October 4-8
Lab Wednesday: Wrap up project 1
Lecture Thursday: Stochastic gradient methods and start discussion of neural networks
For neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. See also chapter 11 and 12 on practicalities and applications
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