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MachineLearningUiO

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This repository contains the educational material on Machine Learning for the courses FYS-STK3155/4155 and FYS5429/9429

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  - 2018_Book_SupervisedLearningWithQuantumC.pdf
  - 978-3-030-93158-2.pdf
  - ADAfaEPoV.pdf
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  - IntroStatLearning.pdf
  - LinearRegressionBook.pdf
  - MathDeepLearning1.pdf
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  - Mehtaetal.pdf
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  - Pattern Classification by Richard O. Duda, David G. Stork, Peter E.Hart .pdf
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  - SebastianRaschkaYuxi_2022_MachineLearningwithPyTorchandScikit-Lear.pdf
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This site contains all material relevant for the courses on Applied
Data Analysis and Machine Learning. The courses are FYS-STK3155/4155 and FYS5429/9429.
The link to the textbook as jupyter-book is at https://github.com/EducationalMaterialUiO/MachineLearningUiO/blob/main/doc/LectureNotes/_build/html/index.html
and the PDF file is at https://github.com/EducationalMaterialUiO/MachineLearningUiO/blob/main/doc/BookML/LecturesBook.pdf
All lectures can be accessed either in person or live via zoom at https://uio.zoom.us/my/mortenhj
All lectures will be recorded.

## 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
focus 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, Julia 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.
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

1. Statistical analysis and optimization of data
2. Machine learning algorithms and Deep Learning

### Statistical analysis and optimization of data

The following topics are normally 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 (not all) are typically  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 and generative models
- Unsupervised learning Dimensionality reduction, PCA, k-means and  clustering
- Autoenconders
- Generative algorithms

Not all these topics may be covered by FYS-STK3155/4155. Some of them, like generative models and Bayesian statistics are covered by the advanced course FYS5429.

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.

## Instructor information

##  Teaching team Fall 2026
* Morten Hjorth-Jensen, mhjensen@uio.no
  * _Phone_: +47-48257387
  * _Office_: Department of Physics, room FØ470 
  * _Office hours_: *Anytime*! Individual or group office hours can be performed either in person or via zoom. Feel free to send an email/text for planning.
* Amanda Jean Bowerman, amandajb@fys.uio.no
* Viktor Svensson, viktor.svensson@fys.uio.no
* Amund Solum Alsvik, amundsal@student.matnat.uio.no
* Britt S. Haanen, b.s.m.haanen@fys.uio.no
* Kim Graaturd, kim.graatrud@fys.uio.no

## Practicalities

1. The sessions on Wednesdays last four hours for each group (four in total) and the focus is work on exercices and projects.  The sessions will begin with lectures and questions and answers about the material to be covered every week.
2. There are two groups, Wednesdays 815am-12pm and 1215pm-4pm. Please sign up as soon as possible for one of the groups. Max capacity per group is 30-40 participants.

3. Lectures are Mondays and Tuesdays. The lectures combine a mix of traditional lectures with an active learning environment. These lectures are recorded. 
4. Three projects which are graded and count 1/3 each of the final grade;
5. A selected number of weekly assignments. The weekly assignments can be handed in and for all assignments you can get an extra score of 20 points to the final grade.
6. 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;
7. 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:

* 95-100 points: A
 * 85-94 points: B
 * 72-84 points: C
 * 64-71 points: D
 * 60-63 points: E
 * 0-59 points: F-failed

In addition you can get an extra score for weekly assignments (10 in total and due each Friday). Each weekly assignment counts 1 point. As an example, this means that if your average after three projects is 88 points and you have handed in and gotten approved seven weekly exercises, the total score is 88+7=95, which translates into an A.

## Tentative deadllines for projects

* First project, due October 5, counts 1/3 of final grade
* Second  project, due November 2, counts 1/3 of final grade
* Third project, due December 7, counts 1/3 of final grade
* Extra Credit (not mandatory),  weekly exercise assignments, 10 in total (due Fridays), adds one point per exercises set.

## 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

* pip install numpy scipy matplotlib ipython scikit-learn mglearn sympy pandas pillow

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 python

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 python

You can specify the python version you wish to install.

For various dependencies, we recommend installing a light variant of conda.

### 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

* Anaconda:https://docs.anaconda.com/,

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_.

* Enthought canopy:https://www.enthought.com/product/canopy/

is a Python
distribution for scientific and analytic computing distribution and
analysis environment, available for free and under a commercial
license.

Furthermore, Google's Colab:https://colab.research.google.com/notebooks/welcome.ipynb is a free Jupyter notebook environment that requires 
no setup and runs entirely in the cloud. Try it out!

### Useful Python libraries
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
* _JAX_" https://jax.readthedocs.io/en/latest/index.html has now more or less replaced _Autograd_.
  JAX is Autograd and XLA, brought together for high-performance numerical computing and machine learning research.
  It provides composable transformations of Python+NumPy programs: differentiate, vectorize, parallelize, Just-In-Time compile to GPU/TPU, and more.
* _SymPy_:https://www.sympy.org/en/index.html is a Python library for symbolic mathematics.

* _SymPy_:https://www.sympy.org/en/index.html is a Python library for symbolic mathematics. 
* _scikit-learn_:https://scikit-learn.org/stable/ has simple and efficient tools for machine learning, data mining and data analysis
* _TensorFlow_:https://www.tensorflow.org/ is a Python library for fast numerical computing created and released by Google
* _Keras_:https://keras.io/ is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano
* And many more such as _pytorch_:https://pytorch.org/,  _Theano_:https://pypi.org/project/Theano/ etc

## Textbooks

_Recommended textbooks_:

In addition to the lecture notes listed above, we recommend the books of Goodfellow et al. and Raschka 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.
- Sebastian Raschka, Yuxi Lie, and Vahid Mirjalili,  Machine Learning with PyTorch and Scikit-Learn at https://www.packtpub.com/product/machine-learning-with-pytorch-and-scikit-learn/9781801819312, see also https://sebastianraschka.com/blog/2022/ml-pytorch-book.html

The text by Raschka et al. is well-adapted to this course and contains many coding examples. 
The weekly plans will include reading suggestions from these two textbooks. In addition, you may find the following textbooks interesting.

_Additional textbooks_:
- Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, https://www.springer.com/gp/book/9780387310732. This is the main textbook and this course covers chapters 1-7, 11 and 12. You can download for free the textbook in PDF format at https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
- Kevin Murphy, Probabilistic Machine Learning, an Introduction, https://probml.github.io/pml-book/book1.html
- 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.
- Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly, https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/. This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.
- David Foster, Generative Deep Learning, https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/
- Babcock and Gavras, Generative AI with Python and TensorFlow, https://github.com/PacktPublishing/Hands-On-Generative-AI-with-Python-and-TensorFlow-2

_General learning book on statistical analysis_:
- Christian Robert and George Casella, Monte Carlo Statistical Methods, Springer
- Peter Hoff, A first course in Bayesian statistical models, Springer

## Links to relevant courses at the University of Oslo

- _FYS5429 Advanced Machine Learning for the Physical Sciences_ https://www.uio.no/studier/emner/matnat/fys/FYS5429/index-eng.html
- _FYS5419 Quantum Computing and Quantum Machine Learning_ https://www.uio.no/studier/emner/matnat/fys/FYS5419/index-eng.html
- _STK2100 Machine learning and statistical methods for prediction and classification_ http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html.  
- _IN3050/4050 Introduction to Artificial Intelligence and Machine Learning_ https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html. Introductory course in machine learning and AI with an algorithmic approach. 
- _IN4080 Natural Language Processing_ https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html. Probabilistic and machine learning techniques applied to natural language processing. 
- _IN5550 – Neural Methods in Natural Language Processing_ https://www.uio.no/studier/emner/matnat/ifi/IN5550/index.html. This course studies a selection of advanced techniques in Natural Language Processing (NLP), with particular emphasis on recent and current research literature. The focus will be on machine learning and specifically deep neural network approaches to the automated analysis of natural language text.
- _STK-IN4300 Statistical learning methods in Data Science_ https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.
- _IN-STK5000  Adaptive Methods for Data-Based Decision Making_ https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html. Methods for adaptive collection and processing of data based on machine learning techniques. 
- _IN4310 Deep Learning for Image Analysis_ https://www.uio.no/studier/emner/matnat/ifi/IN4310/index.html. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.
- _STK4051 Computational Statistics_ https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html
- _STK4021 Applied Bayesian Analysis and Numerical Methods_ https://www.uio.no/studier/emner/matnat/math/STK4021/

MachineLearningUiO

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