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update week 34

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Morten Hjorth-Jensen
2023-08-21 09:01:58 +02:00
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@@ -4,6 +4,8 @@ DATE: Week 34, August 21-25, 2021
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===== Overview of first week =====
@@ -40,7 +42,7 @@ The labs are also available till 6pm Tuesdays and Wednesdays. Videos and learnin
* The sessions on Tuesdays and Wednesdays last four hours and will include partly lectures in a flipped mode (promoting active learning) and work on exercices and projects.
* Thursdays: regular lectures (12.15pm-2pm)
* Weekly reading assignments and videos needed to solve projects and exercises.
* Weekly exercises when not working on projects. You can hand in exercises if you want and get an extra score, see below.
* Weekly exercises. You can hand in exercises if you want and get an extra score, see below.
* Detailed lecture notes, exercises, all programs presented, projects etc can be found at the homepage of the course.
* Weekly plans and all other information are on the official website. This info will also be conveyed via weekly emails.
* No final exam, three projects that are graded and have to be approved.
@@ -62,7 +64,7 @@ The labs are also available till 6pm Tuesdays and Wednesdays. Videos and learnin
o For the last project each group/participant submits a proposal or works with suggested (by us) proposals for the project.
o If possible, we would like to organize the last project as a workshop where each group presents this to all other participants of the course
o Based on feedback etc, each group finalizes the report and submits for grading.
* Python is the default programming language, but feel free to use C/C++ and/or Fortran or other programming languages. All source codes discussed during the lectures can be found at the webpage and "github address":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs" of the course.
* Python is the default programming language, but feel free to use C/C++, Julia and/or Fortran or other programming languages. All source codes discussed during the lectures can be found at the webpage and "github address":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs" of the course.
@@ -106,17 +108,17 @@ Extra Credit (not mandatory), weekly exercise assignments, 10 in total (due Fri
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The lecture notes are collected as a jupyter-book at URL:"https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html".
In addition to the lecture notes, we recommend the books of Bishop, Hastie et al, Murphy and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these texts. The text by Hastie et al is also widely used in the Machine Learning community. Finally, we also recommend the hands-on text by Geron, see next slide.
In addition to the lecture notes, we recommend the books of Bishop, Hastie et al, Murphy and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these texts. The text by Hastie et al is also widely used in the Machine Learning community. Finally, we also recommend the hands-on text by Geron, see next slide for links.
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===== Textbooks =====
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* "Goodfellow, Bengio, and Courville (GBC), Deep Learning":"https://www.deeplearningbook.org/"
* Christopher M. Bishop (CB), Pattern Recognition and Machine Learning
* Hastie, Tibshirani, and Friedman (HTF), The Elements of Statistical Learning, Springer, URL:"https://www.springer.com/gp/book/9780387848570". This is a well-known text and serves as additional literature.
* Aurelien Geron (AG), 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.
* Hastie, Tibshirani, and Friedman (HTF), The Elements of Statistical Learning, Springer, URL:"https://www.springer.com/gp/book/9780387848570".* Aurelien Geron (AG), 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.
* "Kevin Murphy (KM), Probabilistic Machine Learning, an Introduction":"https://probml.github.io/pml-book/book1.html"
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@@ -219,3 +221,38 @@ Hands-on demonstrations, exercises and projects aim at deepening your understand
* "STK4021 Applied Bayesian Analysis and Numerical Methods":"https://www.uio.no/studier/emner/matnat/math/STK4021/index-eng.html"
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===== 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
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===== More on learning outcomes =====
* 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;
* Generative models
* 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) or Julia or other.