diff --git a/doc/LectureNotes/_config.yml b/doc/LectureNotes/_config.yml
new file mode 100644
index 000000000..b5eb031b5
--- /dev/null
+++ b/doc/LectureNotes/_config.yml
@@ -0,0 +1,72 @@
+# Book settings
+title: Applied Data Analysis and Machine Learning
+author: Morten Hjorth-Jensen
+logo: logo.png
+
+latex:
+ latex_documents:
+ targetname: book.tex
+
+repository:
+ url : https://github.com/mhjensen/Physics321/doc/LectureNotes/_build/html/index.html
+
+html:
+ use_repository_button: true
+
+launch_buttons:
+ binderhub_url : https://mybinder.org
+
+html:
+ use_issues_button: true
+
+execute:
+ execute_notebooks : auto # Whether to execute notebooks at build time. Must be one of ("auto", "force", "cache", "off")
+ cache : "" # A path to the jupyter cache that will be used to store execution artifacs. Defaults to `_build/.jupyter_cache/`
+ exclude_patterns : [] # A list of patterns to *skip* in execution (e.g. a notebook that takes a really long time)
+ timeout : 30 # The maximum time (in seconds) each notebook cell is allowed to run.
+ run_in_temp : false # If `True`, then a temporary directory will be created and used as the command working directory (cwd),
+ # otherwise the notebook's parent directory will be the cwd.
+ allow_errors : false # If `False`, when a code cell raises an error the execution is stopped, otherwise all cells are always run.
+ stderr_output : show # One of 'show', 'remove', 'remove-warn', 'warn', 'error', 'severe'
+
+
+# Parse and render settings
+parse:
+ myst_extended_syntax : false # enable MyST extended syntax support (see documents for details)
+ myst_url_schemes : [mailto, http, https] # URI schemes that will be recognised as external URLs in Markdown links
+
+
+# HTML-specific settings
+html:
+ favicon : "" # A path to a favicon image
+ use_edit_page_button : false # Whether to add an "edit this page" button to pages. If `true`, repository information in repository: must be filled in
+ use_repository_button : false # Whether to add a link to your repository button
+ use_issues_button : false # Whether to add an "open an issue" button
+ extra_navbar : Powered by Jupyter Book # Will be displayed underneath the left navbar.
+ extra_footer : "" # Will be displayed underneath the footer.
+ google_analytics_id : "" # A GA id that can be used to track book views.
+ home_page_in_navbar : true # Whether to include your home page in the left Navigation Bar
+ baseurl : "" # The base URL where your book will be hosted. Used for creating image previews and social links. e.g.: https://mypage.com/mybook/
+ comments:
+ hypothesis : false
+ utterances : false
+
+#######################################################################################
+# LaTeX-specific settings
+latex:
+ latex_engine : pdflatex # one of 'pdflatex', 'xelatex' (recommended for unicode), 'luatex', 'platex', 'uplatex'
+
+#######################################################################################
+# Launch button settings
+launch_buttons:
+ notebook_interface : classic # The interface interactive links will activate ["classic", "jupyterlab"]
+ binderhub_url : https://mybinder.org # The URL of the BinderHub (e.g., https://mybinder.org)
+# jupyterhub_url : "" # The URL of the JupyterHub (e.g., https://datahub.berkeley.edu)
+ thebe : false # Add a thebe button to pages (requires the repository to run on Binder)
+# colab_url : "" # The URL of Google Colab (https://colab.research.google.com)
+
+repository:
+ url : https://github.com/mhjensen/Physics321/doc/LectureNotes/_build/html/index.html # The URL to your book's repository
+# path_to_book : "" # A path to your book's folder, relative to the repository root.
+ branch : master # Which branch of the repository should be used when creating links
+
diff --git a/doc/LectureNotes/_config.yml~ b/doc/LectureNotes/_config.yml~
new file mode 100644
index 000000000..2080e6a54
--- /dev/null
+++ b/doc/LectureNotes/_config.yml~
@@ -0,0 +1,72 @@
+# Book settings
+title: Classical mechanics
+author: Morten Hjorth-Jensen and Scott Pratt
+logo: logo.png
+
+latex:
+ latex_documents:
+ targetname: book.tex
+
+repository:
+ url : https://github.com/mhjensen/Physics321/doc/LectureNotes/_build/html/index.html
+
+html:
+ use_repository_button: true
+
+launch_buttons:
+ binderhub_url : https://mybinder.org
+
+html:
+ use_issues_button: true
+
+execute:
+ execute_notebooks : auto # Whether to execute notebooks at build time. Must be one of ("auto", "force", "cache", "off")
+ cache : "" # A path to the jupyter cache that will be used to store execution artifacs. Defaults to `_build/.jupyter_cache/`
+ exclude_patterns : [] # A list of patterns to *skip* in execution (e.g. a notebook that takes a really long time)
+ timeout : 30 # The maximum time (in seconds) each notebook cell is allowed to run.
+ run_in_temp : false # If `True`, then a temporary directory will be created and used as the command working directory (cwd),
+ # otherwise the notebook's parent directory will be the cwd.
+ allow_errors : false # If `False`, when a code cell raises an error the execution is stopped, otherwise all cells are always run.
+ stderr_output : show # One of 'show', 'remove', 'remove-warn', 'warn', 'error', 'severe'
+
+
+# Parse and render settings
+parse:
+ myst_extended_syntax : false # enable MyST extended syntax support (see documents for details)
+ myst_url_schemes : [mailto, http, https] # URI schemes that will be recognised as external URLs in Markdown links
+
+
+# HTML-specific settings
+html:
+ favicon : "" # A path to a favicon image
+ use_edit_page_button : false # Whether to add an "edit this page" button to pages. If `true`, repository information in repository: must be filled in
+ use_repository_button : false # Whether to add a link to your repository button
+ use_issues_button : false # Whether to add an "open an issue" button
+ extra_navbar : Powered by Jupyter Book # Will be displayed underneath the left navbar.
+ extra_footer : "" # Will be displayed underneath the footer.
+ google_analytics_id : "" # A GA id that can be used to track book views.
+ home_page_in_navbar : true # Whether to include your home page in the left Navigation Bar
+ baseurl : "" # The base URL where your book will be hosted. Used for creating image previews and social links. e.g.: https://mypage.com/mybook/
+ comments:
+ hypothesis : false
+ utterances : false
+
+#######################################################################################
+# LaTeX-specific settings
+latex:
+ latex_engine : pdflatex # one of 'pdflatex', 'xelatex' (recommended for unicode), 'luatex', 'platex', 'uplatex'
+
+#######################################################################################
+# Launch button settings
+launch_buttons:
+ notebook_interface : classic # The interface interactive links will activate ["classic", "jupyterlab"]
+ binderhub_url : https://mybinder.org # The URL of the BinderHub (e.g., https://mybinder.org)
+# jupyterhub_url : "" # The URL of the JupyterHub (e.g., https://datahub.berkeley.edu)
+ thebe : false # Add a thebe button to pages (requires the repository to run on Binder)
+# colab_url : "" # The URL of Google Colab (https://colab.research.google.com)
+
+repository:
+ url : https://github.com/mhjensen/Physics321/doc/LectureNotes/_build/html/index.html # The URL to your book's repository
+# path_to_book : "" # A path to your book's folder, relative to the repository root.
+ branch : master # Which branch of the repository should be used when creating links
+
diff --git a/doc/LectureNotes/_toc.yml b/doc/LectureNotes/_toc.yml
new file mode 100644
index 000000000..b39daaede
--- /dev/null
+++ b/doc/LectureNotes/_toc.yml
@@ -0,0 +1,25 @@
+- file: intro
+- part: About the course
+ chapters:
+ - file: schedule
+ - file: teachers
+ - file: textbooks
+- part: From Regression to Support Vector Machines
+ numbered: true
+ chapters:
+ - file: chapter1.ipynb
+ - file: chapter2.ipynb
+ - file: chapter3.ipynb
+ - file: chapter4.ipynb
+- part: Deep Learning
+ numbered: true
+ chapters:
+ - file: chapter5.ipynb
+ - file: chapter6.ipynb
+ - file: chapter7.ipynb
+ - file: chapter8.ipynb
+- part: Trees and Ensemble Methods
+ numbered: true
+ chapters:
+ - file: chapter9.ipynb
+ - file: chapter10.ipynb
diff --git a/doc/LectureNotes/_toc.yml~ b/doc/LectureNotes/_toc.yml~
new file mode 100644
index 000000000..b4fb57ed2
--- /dev/null
+++ b/doc/LectureNotes/_toc.yml~
@@ -0,0 +1,20 @@
+- file: intro
+- part: About the course
+ chapters:
+ - file: schedule
+ - file: teachers
+ - file: textbooks
+- part: Introductory Material
+ numbered: true
+ chapters:
+ - file: chapter1.ipynb
+ - file: chapter2.ipynb
+ - file: chapter3.ipynb
+ - file: chapter4.ipynb
+- part: Advanced Topics
+ numbered: true
+ chapters:
+ - file: chapter5.ipynb
+ - file: chapter6.ipynb
+ - file: chapter7.ipynb
+ - file: chapter8.ipynb
diff --git a/doc/LectureNotes/content.md b/doc/LectureNotes/content.md
new file mode 100644
index 000000000..0f6aca77a
--- /dev/null
+++ b/doc/LectureNotes/content.md
@@ -0,0 +1,5 @@
+Content in Jupyter Book
+=======================
+
+There are many ways to write content in Jupyter Book. This short section
+covers a few tips for how to do so.
diff --git a/doc/LectureNotes/intro.md b/doc/LectureNotes/intro.md
new file mode 100644
index 000000000..811e85085
--- /dev/null
+++ b/doc/LectureNotes/intro.md
@@ -0,0 +1,145 @@
+# 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 about 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, normally three, 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, although we will try to link it 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;
+- 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, 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
+
+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 Jupiter notebook
+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.
+
+
+## The course has two central parts
+
+1. Statistical analysis and optimization of data
+2. Machine learning
+
+These topics will be scattered thorughout the course and may not necessarily be taught separately. Rather, we will often take an approach (during the lectures and project/exercise sessions) where say elements from statistical data analysis are mixed with specific Machine Learning algorithms.
+
+### 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, from PCA to cluster models
+
+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 varous algorithms discussed during the lectures. We recommend strongly to form small project groups of 2-3 participants, if possible.
+
+## 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.
+
+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 mainly
+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 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
+
+* 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
+* _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
+
diff --git a/doc/LectureNotes/intro.md~ b/doc/LectureNotes/intro.md~
new file mode 100644
index 000000000..0c3dd4b10
--- /dev/null
+++ b/doc/LectureNotes/intro.md~
@@ -0,0 +1,41 @@
+# PHY321 Classical Mechanics I, Michigan State University, Spring 2021
+
+This is the Jupyter-Book for the Classical Mechanics course at MSU
+
+
+
+## Learning outcomes
+
+After the course you should:
+
+- be able to analyze forces that act on objects, apply Newton’s laws to determine the equations of motion, and solve these analytically and numerically,
+- Know about inertial frames and their relation to accelerating and rotating frames (non-inertial frames)
+- Know about forces, work, energy, angular momentum, linear momentum and conservation laws
+- Know about various types of motions, falling objects, objects moving in various fields
+- Know how to analyze energy diagrams and defining effective potential
+- Have knowledge about small oscillations, Harmonic oscillator potential and equations of motion
+- Have knowledge about transformation of variables that allow for analytical solutions, example two-body problems
+- Have knowledge about central forces and two-body problems, center-of-mass and relative coordinates as reference frame
+- Have knowledge about two-body scattering problems, classical scattering cross section
+- Have knowledge about Variational calculus and Lagrangian formalism
+- Know how to derive the equations of motion from the Lagrangian formalism with and without constraints (Lagrangian multipliers)
+
+To solve many of these problems, we have through different projects and weekly exercises studied many systems numerically, from falling objects with and without friction/air resistance, small oscillations (harmonic oscillator), gravitational problems and other central force problems, rotations and the classical pendulum. To solve these systems, we have applied different algorithms for solving differential equations. These are
+- Euler-Cromer and Velocity-Verlet as energy conserving algorithms (time-independent forces)
+- Runge-Kutta family of algorithms for time-dependent forces
+We have also, in connection with for example the work-energy theorem studied methods for evaluating integrals. These are
+- Numerical integration using the Trapezoidal, midpoint and Simpson's rule.
+
+You should also have acquired skills in structuring a numerical project, as well as having developed a critical understanding of the pros and cons of the methods and an understanding of their limits and what can go wrong. Computing means solving scientific problems using computers. It covers numerical as well as symbolic computing. Computing is also about developing an understanding of the scientific process by enhancing algorithmic thinking when solving problems. Computing competence has
+always been a central part of the science and engineering education.
+In particular, some of the competences that are important in the development of your own understanding of
+computations, we would like to emphasize
+- derivation, verification, and implementation of algorithms
+- understanding what can go wrong with algorithms
+- overview of important, known algorithms for solving mechanics problems (To a extent large differential equations and integration)
+- understanding how algorithms are used to solve mathematical problems
+- Making science (your results) reproducible
+- algorithmic thinking for gaining deeper insights about scientific problems
+
+
+
diff --git a/doc/LectureNotes/logo.png b/doc/LectureNotes/logo.png
new file mode 100644
index 000000000..4ab529a39
Binary files /dev/null and b/doc/LectureNotes/logo.png differ
diff --git a/doc/LectureNotes/references.bib b/doc/LectureNotes/references.bib
new file mode 100644
index 000000000..783ec6aa5
--- /dev/null
+++ b/doc/LectureNotes/references.bib
@@ -0,0 +1,56 @@
+---
+---
+
+@inproceedings{holdgraf_evidence_2014,
+ address = {Brisbane, Australia, Australia},
+ title = {Evidence for {Predictive} {Coding} in {Human} {Auditory} {Cortex}},
+ booktitle = {International {Conference} on {Cognitive} {Neuroscience}},
+ publisher = {Frontiers in Neuroscience},
+ author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Knight, Robert T.},
+ year = {2014}
+}
+
+@article{holdgraf_rapid_2016,
+ title = {Rapid tuning shifts in human auditory cortex enhance speech intelligibility},
+ volume = {7},
+ issn = {2041-1723},
+ url = {http://www.nature.com/doifinder/10.1038/ncomms13654},
+ doi = {10.1038/ncomms13654},
+ number = {May},
+ journal = {Nature Communications},
+ author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Rieger, Jochem W. and Crone, Nathan and Lin, Jack J. and Knight, Robert T. and Theunissen, Frédéric E.},
+ year = {2016},
+ pages = {13654},
+ file = {Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:C\:\\Users\\chold\\Zotero\\storage\\MDQP3JWE\\Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:application/pdf}
+}
+
+@inproceedings{holdgraf_portable_2017,
+ title = {Portable learning environments for hands-on computational instruction using container-and cloud-based technology to teach data science},
+ volume = {Part F1287},
+ isbn = {978-1-4503-5272-7},
+ doi = {10.1145/3093338.3093370},
+ abstract = {© 2017 ACM. There is an increasing interest in learning outside of the traditional classroom setting. This is especially true for topics covering computational tools and data science, as both are challenging to incorporate in the standard curriculum. These atypical learning environments offer new opportunities for teaching, particularly when it comes to combining conceptual knowledge with hands-on experience/expertise with methods and skills. Advances in cloud computing and containerized environments provide an attractive opportunity to improve the effciency and ease with which students can learn. This manuscript details recent advances towards using commonly-Available cloud computing services and advanced cyberinfrastructure support for improving the learning experience in bootcamp-style events. We cover the benets (and challenges) of using a server hosted remotely instead of relying on student laptops, discuss the technology that was used in order to make this possible, and give suggestions for how others could implement and improve upon this model for pedagogy and reproducibility.},
+ booktitle = {{ACM} {International} {Conference} {Proceeding} {Series}},
+ author = {Holdgraf, Christopher Ramsay and Culich, A. and Rokem, A. and Deniz, F. and Alegro, M. and Ushizima, D.},
+ year = {2017},
+ keywords = {Teaching, Bootcamps, Cloud computing, Data science, Docker, Pedagogy}
+}
+
+@article{holdgraf_encoding_2017,
+ title = {Encoding and decoding models in cognitive electrophysiology},
+ volume = {11},
+ issn = {16625137},
+ doi = {10.3389/fnsys.2017.00061},
+ abstract = {© 2017 Holdgraf, Rieger, Micheli, Martin, Knight and Theunissen. Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model-based methods for asking neuroscience questions, allowing scientists to investigate multiple hypotheses with a single dataset, to use complex, time-varying stimuli, and to study the human brain under more naturalistic conditions. These tools come in the form of “Encoding” models, in which stimulus features are used to model brain activity, and “Decoding” models, in which neural features are used to generated a stimulus output. Here we review the current state of encoding and decoding models in cognitive electrophysiology and provide a practical guide toward conducting experiments and analyses in this emerging field. Our examples focus on using linear models in the study of human language and audition. We show how to calculate auditory receptive fields from natural sounds as well as how to decode neural recordings to predict speech. The paper aims to be a useful tutorial to these approaches, and a practical introduction to using machine learning and applied statistics to build models of neural activity. The data analytic approaches we discuss may also be applied to other sensory modalities, motor systems, and cognitive systems, and we cover some examples in these areas. In addition, a collection of Jupyter notebooks is publicly available as a complement to the material covered in this paper, providing code examples and tutorials for predictive modeling in python. The aimis to provide a practical understanding of predictivemodeling of human brain data and to propose best-practices in conducting these analyses.},
+ journal = {Frontiers in Systems Neuroscience},
+ author = {Holdgraf, Christopher Ramsay and Rieger, J.W. and Micheli, C. and Martin, S. and Knight, R.T. and Theunissen, F.E.},
+ year = {2017},
+ keywords = {Decoding models, Encoding models, Electrocorticography (ECoG), Electrophysiology/evoked potentials, Machine learning applied to neuroscience, Natural stimuli, Predictive modeling, Tutorials}
+}
+
+@book{ruby,
+ title = {The Ruby Programming Language},
+ author = {Flanagan, David and Matsumoto, Yukihiro},
+ year = {2008},
+ publisher = {O'Reilly Media}
+}
diff --git a/doc/LectureNotes/schedule.md b/doc/LectureNotes/schedule.md
new file mode 100644
index 000000000..13527dc67
--- /dev/null
+++ b/doc/LectureNotes/schedule.md
@@ -0,0 +1,14 @@
+# Teaching schedule with links to material
+
+
+This course will be delivered in a hybrid mode, with online lectures and on site or online laboratory sessions.
+
+1. Four lectures per week, Fall semester, 10 ECTS. The lectures will be fully online. The lectures will be recorded and linked to this site and the official University of Oslo website for the course;
+2. 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;
+3. Three projects which are graded and count 1/3 each of the final grade;
+4. A selected number of weekly assignments;
+5. 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;
+6. The course is offered as a FYS-MAT4155 (Master of Science level) and a FYS-MAT3155 (senior undergraduate) course;
+7. Videos of teaching material are available via the links at https://compphysics.github.io/MachineLearning/doc/web/course.html;
+8. Weekly emails with summary of activities will be mailed to all participants;
+
diff --git a/doc/LectureNotes/schedule.md~ b/doc/LectureNotes/schedule.md~
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+# Teaching schedule with links to material
+
+
+### Week 2, January 11-15, 2021
+This week is meant as a review and reading week. There are no lectures but we have, in addition to videos to watch, some simple recommendations on material to review. Note that we will start with these topics on Wednesday the 20th. If you don't have the possibility to look this material prior to our first lecture, a good fraction of this material is discussed during the first lecture.
+
+
+- Introduction to the course and reminder on vectors, space, time and motion, JRT chapters 1.2 and 1.3 and lecture notes (https://mhjensen.github.io/Physics321/doc/pub/week2/html/week2.html). Watch the video at https://mediaspace.msu.edu/media/t/1_zzl90pfu
+- Python programming reminder, elements from CMSE 201 and how they are used in this course. Installing software (anaconda). See slides at https://mhjensen.github.io/Physics321/doc/pub/week2/html/week2.html).
+- Introduction to Git and GitHub/GitLab. Watch the video at https://mediaspace.msu.edu/media/t/1_8mgx3cyf
+
+For solving this exercise, AMS chapters 2 and 4 are very useful as review material. First homework set is available and due Monday January 25. See https://mhjensen.github.io/Physics321/doc/web/course.html (scroll down to homework 1). Parts of the homework will be discussed next week (Friday 22).
+
+
+### Week 3, January 18-22, 2021
+
+- Monday 1/18: MLK day, no lecture
+- Wednesday 1/20: Getting started and overview of course. JRT chapters 1.2 and 1.3 and lecture notes (https://mhjensen.github.io/Physics321/doc/pub/week2/html/week2.html). AMS chapters 2 and 4 are very useful. 1st homework due January 25.
+Video of Lecture at https://mediaspace.msu.edu/media/t/1_xwtfo4i1 (some minor problems in the beginning of video).
+- Friday 1/22: Forces and Newton's laws of motion. Free fall problems. JRT chapter 1.4 and lecture notes (https://mhjensen.github.io/Physics321/doc/pub/week3/html/week3.html). AMS chapters 2 and 4 are very useful Homework 1 is due on January 25 (Monday).
+Video of lecture at https://mediaspace.msu.edu/media/t/1_ajqd2f42.
+Handwritten notes at https://github.com/mhjensen/Physics321/blob/master/doc/HandWrittenNotes/Spring2021/NotesJan22.pdf
+- Solution to homeworks are in https://d2l.msu.edu/ and are posted here as well.
+
+### Week 4, January 25-29, 2021
+- Monday 1/25: Motion and forces, Newton's laws, examples, 2nd homework, due February 1.
+Video of lecture at https://mediaspace.msu.edu/media/t/1_7pyc0yts and handwritten notes at https://github.com/mhjensen/Physics321/blob/master/doc/HandWrittenNotes/Spring2021/NotesJan25.pdf
+- Wednesday 1/27: Motion and forces, Newton's laws, examples
+Video of lecture https://mediaspace.msu.edu/media/t/1_k2acom08 and handwritten notes at https://github.com/mhjensen/Physics321/blob/master/doc/HandWrittenNotes/Spring2021/NotesJan27.pdf
+- Friday 1/29: Motion and forces, Newton's laws, examples.
+Video of lecture https://mediaspace.msu.edu/media/t/1_ieaw1a6y and handwritten notes at https://github.com/mhjensen/Physics321/blob/master/doc/HandWrittenNotes/Spring2021/NotesJan29.pdf
+- Solution to homeworks are in https://d2l.msu.edu/
+- Good reads are Taylor chapters 1.4, 1.5, 1.6, 2.1-2.4 and AMS chapters 4.2 and 5
+- Solutions to homework 1 at https://github.com/mhjensen/Physics321/blob/master/doc/src/Homeworks/Solutions/solutionhw1.ipynb and videos at https://www.youtube.com/playlist?list=PLq9LZ5390Us41TX42XzOPIEPcbHaqX8Hx
+
+### Week 5, February 1-5, 2021
+- Monday 2/1: We discuss various forces and their pertinent equations of motion. Recommended reading: Taylor 2.1-2.4. Malthe-Sørenssen chapter 6-7 contains many examples. We will cover in particular a falling object in two dimensions with linear air resistance relevant for homework 3, due February 8. Video of lecture at https://mediaspace.msu.edu/media/t/1_smo1913z and handwritten notes at https://github.com/mhjensen/Physics321/blob/master/doc/HandWrittenNotes/Spring2021/NotesFeb1.pdf
+- Wednesday 2/3: We discuss other force models with examples such as the gravitational force and a spring force. See Malthe-Sørenssen chapter 7.3-7.5. We start also our discussion of nergy and work, see Taylor 4.1 Video of lecture at https://mediaspace.msu.edu/media/t/1_raywhcdx
+and handwritten notes at https://github.com/mhjensen/Physics321/blob/master/doc/HandWrittenNotes/Spring2021/NotesFeb3.pdf. Julie's notes are at https://github.com/mhjensen/Physics321/blob/master/doc/HandWrittenNotes/Spring2021/JulieNotesHW2.pdf
+- Friday 2/5: We discuss several examples of energy and work. Taylor 4.1-4.3. Video of lecture at https://mediaspace.msu.edu/media/t/1_rtcxs5op and handwritten notes at https://github.com/mhjensen/Physics321/blob/master/doc/HandWrittenNotes/Spring2021/NotesFeb5.pdf
+Solution hw 2 for coding part as Jupyter notebook: https://github.com/mhjensen/Physics321/blob/master/doc/src/Homeworks/Solutions/solutionhw2.ipynb
+Solution for analytical part as handwritten note (exercises 1-5): https://github.com/mhjensen/Physics321/blob/master/doc/src/Homeworks/Solutions/SolutionHW2Spring2021.pdf
+
+
+
+### Week 6, February 8-12, 2021
+- Monday 2/8: More on Conservation laws, momentum conservation, 4th homework, due February 15
+- Wednesday 2/10: Examples of applications of conservation laws, angular momentum conservation. Video of lecture at https://mediaspace.msu.edu/media/t/1_ff1x7fjr and handwritten notes at https://github.com/mhjensen/Physics321/blob/master/doc/HandWrittenNotes/Spring2021/NotesFeb10.pdf
+- Friday 2/12: Conservation laws and further examples. Video of lecture at https://mediaspace.msu.edu/media/t/1_98lgx23c and handwritten notes at
+https://github.com/mhjensen/Physics321/blob/master/doc/HandWrittenNotes/Spring2021/NotesFeb12.pdf
+- Good reads are Taylor sections 3.1-3.5 and 4.1-4.3 and AMS chapters 10-14.
+Solution hw 3 as Jupyter notebook at: https://github.com/mhjensen/Physics321/blob/master/doc/src/Homeworks/Solutions/solutionhw3.ipynb
+
+### Week 7, February 15-19, 2021
+- Monday 2/15: Conservation laws. Introducing the Velocity Verlet algorithm and the Earth Sun problem, 5th homework due February 22.
+Video of lecture at https://mediaspace.msu.edu/media/t/1_mgqtt4lm and handwritten notes at https://github.com/mhjensen/Physics321/blob/master/doc/HandWrittenNotes/Spring2021/NotesFeb15.pdf
+- Wednesday 2/17: Examples of application of conservations laws (see chapter 4 of Taylor).
+- Video of lecture at https://mediaspace.msu.edu/media/t/1_a4u97jro and handwritten notes at https://github.com/mhjensen/Physics321/blob/master/doc/HandWrittenNotes/Spring2021/NotesFeb17.pdf
+- Friday 2/19: Conservative forces, sumamry plus discussion of homeworks. Video at https://mediaspace.msu.edu/media/t/1_dnoiopq0 and handwritten notes at
+- https://github.com/mhjensen/Physics321/blob/master/doc/HandWrittenNotes/Spring2021/NotesFeb19.pdf
+- Good reads are Taylor chapter 4 and AMS chapters 10-14 for the conservation laws and the first sections of chapter 5 of Taylor on oscillations.
+- Solution hw 4 as jupyter-notebook at https://github.com/mhjensen/Physics321/blob/master/doc/src/Homeworks/Solutions/solutionhw4.ipynb
+- Remember also the videos of the solutions at https://www.youtube.com/playlist?list=PLq9LZ5390Us41TX42XzOPIEPcbHaqX8Hx
+### Week 8, February 22-26, 2021
+- Monday 2/22: Oscillations, mathematical details, the sliding block and energy conservation, 6th homework, due March 1. Video of lecture at https://mediaspace.msu.edu/media/t/1_kj8ea37l and handwritten notes at
+- https://github.com/mhjensen/Physics321/blob/master/doc/HandWrittenNotes/Spring2021/NotesFeb22.pdf
+- Wednesday 2/24: Oscillations, damped motion and more mathematical details. Taylor chapter 5.1 and 5.2. Video of lecture at https://mediaspace.msu.edu/media/t/1_3e16h2bz and handwritten notes at
+- https://github.com/mhjensen/Physics321/blob/master/doc/HandWrittenNotes/Spring2021/NotesFeb24.pdf
+- Friday 2/26: Oscillations, resonances and more on damped motion, video of lecture at https://mediaspace.msu.edu/media/t/1_j5ou5khh and handwritten notes at
+- https://github.com/mhjensen/Physics321/blob/master/doc/HandWrittenNotes/Spring2021/NotesFeb26.pdf.
+- First midterm project, available Feb 27/28 and due March 12, 2021
+- Good reads are chapter 5 of Taylor on oscillations.
+- Solution hw 5 as jupyter-notebook at https://github.com/mhjensen/Physics321/blob/master/doc/src/Homeworks/Solutions/solutionhw5.ipynb
+
+### Week 9, March 1-5
+- Monday 3/1: Damped Oscillations, driven oscillations and resonances. Video of Lecture at https://mediaspace.msu.edu/media/t/1_e1qae0lc and handwritten notes at
+- https://github.com/mhjensen/Physics321/blob/master/doc/HandWrittenNotes/Spring2021/NotesMarch1.pdf.
+- Wednesday 3/3: Study day, no lectures
+- Friday 3/5: Resonances and start discussion of Fourier series. Discussion of first midterm. Video of lecture at https://mediaspace.msu.edu/media/t/1_m67hhyi8 and handwritten notes at https://github.com/mhjensen/Physics321/blob/master/doc/HandWrittenNotes/Spring2021/NotesMarch5.pdf.
+- Good reads are sections 5.4-5.7 of Taylor on oscillations.
+- Solution hw 6 as jupyter-notebook at https://github.com/mhjensen/Physics321/blob/master/doc/src/Homeworks/Solutions/solutionhw6.ipynb
+
+
+### Week 10, March 8-12, 2021
+- Monday 3/8: Fourier series and oscillations. Discussion of first midterm. Video of lecture at https://mediaspace.msu.edu/media/t/1_8zktj1wn and handwritten notes at https://github.com/mhjensen/Physics321/blob/master/doc/HandWrittenNotes/Spring2021/NotesMarch8.pdf.
+- Wednesday 3/10: Discussions of oscillations and time-dependent forces. and Discussion of first midterm. Video of lecture at https://mediaspace.msu.edu/media/t/1_dmio1l34 and handwritten notes at https://github.com/mhjensen/Physics321/blob/master/doc/HandWrittenNotes/Spring2021/NotesMarch10.pdf.
+- Friday 3/12: Discussion of first midterm and summary of oscillations. Deadline first midterm at midnight.
+There is no video of Friday's session since the lecture was organized in breakout sessions plus a discussion of the first midterm. Thx to all of you for heroic efforts with the first midterm.
+- Good reads are sections 5.7-5.8 and 8.2-8.4 of Taylor and Lecture notes
+
+
+### Week 11, March 15-19, 2021
+- Monday 3/15: Gravity and central force problems, center of mass coordinates. Lecture notes and Taylor chapter 8. 7th homework due March 22. Video of Lecture at https://mediaspace.msu.edu/media/t/1_x34gdl5o and handwritten notes at https://github.com/mhjensen/Physics321/blob/master/doc/HandWrittenNotes/Spring2021/NotesMarch15.pdf.
+- Wednesday 3/17: Gravity and central force problems. Video of lecture at https://mediaspace.msu.edu/media/t/1_dwy3ekta and and handwritten notes at https://github.com/mhjensen/Physics321/blob/master/doc/HandWrittenNotes/Spring2021/NotesMarch17.pdf.
+- Friday 3/19: Gravity and central force problems, centrifugal barriers. Video of lecture at https://mediaspace.msu.edu/media/t/1_moj8tmsv and and handwritten notes at https://github.com/mhjensen/Physics321/blob/master/doc/HandWrittenNotes/Spring2021/NotesMarch19.pdf. Julie's notes on methods and classes are at https://github.com/mhjensen/Physics321/tree/master/doc/pub/PythonClassesMethods
+
+### Week 12, March 22-26, 2021
+- Monday 3/22: Gravity and central force problems, elliptical orbits and Kepler's laws, 8th homework, due March 29.
+- Wednesday 3/24: Gravity and central force problems, elliptical orbits and two-body scattering examples.
+- Friday 3/26: Elliptical orbits, examples and two-body scattering problems.
+
+### Week 13, March 29-April 2, 2020
+- Monday 3/29: Central force problems, summary and discussion of two-body scattering problems. 9th homework, due April 5.
+- Wednesday 3/31: Two-body scattering. Taylor chapter 14 covers parts of the material.
+- Friday 4/2: Two-body scattering (Taylor chapter 14). Second midterm available, due Friday April 16.
+
+### Week 14, April 5-9, 2021
+- Monday 4/5: Wrapping up two-body scattering and begin Variational calculus and the Euler-Lagrange equations, chapter 6 of Taylor and lecture notes.
+- Wednesday 4/7: Variational Calculus, chapter 6 of Taylor
+- Friday 4/9: Variational calculus and the Euler-Lagrange equations, chapter 6 of Taylor and lecture notes.
+### Week 15, April 12-16, 2021
+- Monday 4/12: Variational calculus and the Euler-Lagrange equations, chapter 6 of Taylor and lecture notes.
+- Wednesday 4/14: Variational calculus and the Euler-Lagrange equations, chapter 6 of Taylor and lecture notes.
+- Friday 4/16: Euler-Lagrange equations and Lagrangian formalism. Taylor chapter 6 and lecture notes. Deadline 2nd midterm at midnight.
+### Week 16, April 19-23, 2021
+- Monday 4/19: Langrangian formalism, discussion of examples. Taylor chapters 6 and 7. 10th homework as extra assignments, due April 26.
+- Wednesday 4/21: Lagrangian formalism, examples such as the linear chain and double pendulum. Summary and discussions of final exam project. _Final exam project project, due April 30_
+- Friday 4/23: Study day
+### Week 17, April 26- April 30, 2021, Finals week
+- Final Exam: The final exam will be a project similar to the two midterm projects. Deadline April 30. We will have Q/A sessions Monday 26 and Wednesday 28 at 2.30pm to 4pm. Other sessions can always be arranged.
+
+
+
+
+
diff --git a/doc/LectureNotes/teachers.md b/doc/LectureNotes/teachers.md
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+# Teachers and Grading
+
+
+## Instructor information
+* _Name_: Morten Hjorth-Jensen
+* _Email_: morten.hjorth-jensen@fys.uio.no
+* _Phone_: +47-48257387
+* _Office_: Department of Physics, University of Oslo, Eastern wing, room FØ470
+* _Office hours_: *Anytime*! In Fall Semester 2020 (FS20), as a rule of thumb office hours are planned via computer or telephone. Individual or group office hours will be performed 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).
+
+
+## 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
+
diff --git a/doc/LectureNotes/teachers.md~ b/doc/LectureNotes/teachers.md~
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+# Teachers and Grading
+
+## Teaching team, grading and other practicalities
+
+| Lectures | | | Location |
+|---------|----|----|----|
+| Monday 3:00-3:50pm| Wednesday 3:00-3:50pm | Friday 3:00-3:50pm | Digital via zoom |
+
+
+| Instructor | Email | Office | Office phone/cellphone |
+|--------------|------|-----|----|
+| _Morten Hjorth-Jensen_ https://github.com/mhjensen | hjensen@msu.edu | Office: NSCL/FRIB 2131 | 5179087290/5172491375/+47-48257387 |
+
+
+
+| Office Hours| |
+|----------|----------|
+| Monday/Wednesday/Friday 4-5:00pm | or immediately after class |
+
+| Homework Grader | Email |
+|--------------|------|
+| _Julie Butler_ | butler@frib.msu.edu |
+
+| Office Hours Julie Butler |
+|------------------|
+| Monday 2pm-3pm |
+| Thursday 11am-12pm |
+
+
+| Learning Assistant | Email |
+|--------------|------|
+| _Jeremy Rebenstock_ | rebensto@msu.edu |
+
+| Office Hours Jeremy Rebenstock |
+|--------------------|
+| Tuesday 1230pm-2pm |
+
+
+| Additional Class | Location |
+|---|----|
+| TBA, if wanted | Digital via zoom|
+
+
+
+### Grading and dates
+
+| Activity | Percentage of total score |
+|------|-----|
+|Homeworks, 9 in total and due Mondays the week after | 20% |
+| First Midterm Project, _due Friday March 12_ | 25% |
+| Second Midterm Project, _due Friday April 16_ | 25% |
+| Final Exam project, _due Friday April 30_ | 30% |
+| Extra Credit Assignment, homework 10, (Due Monday April 26)| 10% |
+
+| Grading scale | | | | | | |
+|-----|-----|-------|------|--------|--------|--------|
+| 4.0(90%)| 3.5(80%)| 3.0(70%)| 2.5(60%)| 2.0(50%)| 1.5(40%)| 1.0(30%)|
+
diff --git a/doc/LectureNotes/textbooks.md b/doc/LectureNotes/textbooks.md
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+# Textbooks
+
+
+_Recommended 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.
+- 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 text.
+- 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.
+
+The books by Bishop and Hastie et al. can be downloaded for free if you access the university library via an IP number of your home university.
+
+
+
+_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
+
+_General Machine Learning Books_:
+- Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press
+- Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer
+- David J.C. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press
+- David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press
+
+## Links to relevant courses at the University of Oslo
+The link here https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/ gives an excellent overview of courses on Machine learning at UiO.
+
+- _STK2100 Machine learning and statistical methods for prediction and classification_ http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html.
+- _IN3050 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.
+- _STK-INF3000/4000 Selected Topics in Data Science_ http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html. The course provides insight into selected contemporary relevant topics within Data Science.
+- _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.
+- _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.
+- _INF4490 Biologically Inspired Computing_ http://www.uio.no/studier/emner/matnat/ifi/INF4490/. An introduction to self-adapting methods also called artificial intelligence or machine learning.
+- _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.
+- _IN5400/INF5860 Machine Learning for Image Analysis_ https://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 systems_ https://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.
+- _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/
+
+
diff --git a/doc/LectureNotes/textbooks.md~ b/doc/LectureNotes/textbooks.md~
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+# Textbooks
+
+## Possible textbooks and lecture notes
+
+_Recommended textbook_:
+- JRT: John R. Taylor, Classical Mechanics (Univ. Sci. Books 2005), https://www.uscibooks.com/taylor2.htm, see also https://github.com/mhjensen/Physics321/tree/master/doc/Literature
+_Additional textbooks_:
+- AMS: Anders Malthe-Sørenssen, Elementary Mechanics using Python (Springer 2015), https://www.springer.com/gp/book/9783319195957 and https://github.com/mhjensen/Physics321/tree/master/doc/Literature
+- _Lecture notes_: Posted lecture notes are in the doc/pub folder here or at https://mhjensen.github.io/Physics321/doc/web/course.html for easier viewing. They are not meant to be a replacement for textbook. These notes are updated on a weekly basis and a _git pull_ should thus always give you the latest update.
+