Morten Hjorth-Jensen
169 lines
5.1 KiB
Plaintext
169 lines
5.1 KiB
Plaintext
TITLE: Applied Data Analysis and Machine Learning: Introduction to the course
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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
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DATE: today
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!split
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===== Overview of first week =====
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!bblock
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* Thursday: First lecture: Presentation of the course, aims and content
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* Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra
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* Friday:
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* Computer lab: Wednesday. First time: Thursday and Friday this week, Presentation of hardware and software at room FV329 first hour of every labgroup and solution of first simple exercises. The first two weeks we focus on simple programming exercises and to set up github and QTcreator. This week we discuss how to set up git and obtain a github account and look at two simple programming exercises and for those interested start with project 1.
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!eblock
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!split
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===== Reading suggestions and exercises =====
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!bblock
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* Read sections
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*
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*
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*
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!eblock
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!split
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===== Lectures and ComputerLab =====
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!bblock
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* Lectures: Thursday (8.15am-10am) and Friday (8.15am-10am).
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* Weekly reading assignments needed to solve projects.
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* First hour of each lab session may be used to discuss technicalities, address questions etc linked with projects.
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* Detailed lecture notes, exercises, all programs presented, projects etc can be found at the homepage of the course.
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* Computerlab: Thursday (10am-6pm) and Friday (10am-6pm) room FV329. We may extend to Monday
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* Weekly plans and all other information are on the official webpage.
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* No final exam, the last three projects are graded. In total five projects which all have to be approved.
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!eblock
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!split
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===== Course Format =====
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* Five compulsory projects. Electronic reports only using "devilry":"https://devilry.ifi.uio.no/" to hand in projects and "Git":"https://github.com/" for repository and all your material.
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* Evaluation and grading: The last three projects are graded and each counts 1/3 of the final mark. No final written or oral exam.
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* The computer lab (room FV329)consists of 16 Linux PCs, but many prefer own laptops. C/C++ is the default programming language, but Fortran2008 and Python are also used. All source codes discussed during the lectures can be found at the webpage and "github address":"https://github.com/CompPhysics/ComputationalPhysics1/tree/master/doc/Programs" of the course. We recommend either C/C++, Fortran2008 or Python as languages.
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!eblock
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!split
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===== Teachers and ComputerLab =====
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_Teachers :_
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o "Anna Gribovskaya":"https://www.mn.uio.no/fysikk/?vrtx=person-view&uid=annagrib"
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o "Morten Hjorth-Jensen":"http://mhjgit.github.io/info/doc/web/"
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o "Anders Johansson":"https://www.mn.uio.no/fysikk/?vrtx=person-view&uid=anjohan"
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o "Mathias M. Vege":"https://www.mn.uio.no/fysikk/?vrtx=person-view&uid=hmvege"
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o "Sebastian G. Winther-Larsen":"https://www.mn.uio.no/fysikk/?vrtx=person-view&uid=sebastwi"
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|------------------------------------------------------|
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| day | teacher |
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|---------l-----------------------l--------------------|
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| Group 1: Thursday 10am-2pm | Anders, Anna, Mathias, MHJ, Sebastian |
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| Group 2: Thursday 2pm-6pm | Anders, Anna, Mathias, MHJ, Sebastian |
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| Group 3: Friday 10am-2pm | Anders, Anna, Mathias, MHJ, Sebastian |
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| Group 4: Friday 2pm-6pm | Anders, Anna, Mathias, MHJ, Sebastian |
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|------------------------------------------------------|
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!eblock
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!split
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===== Deadlines for projects (end of day) =====
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!bblock
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o Project 1: September 10 (not graded, only feedback)
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o Project 2: October 1 (not graded, only feedback)
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o Project 3: October 22 (graded with feedback)
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o Project 4: November 12 (graded with feedback)
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o Project 5: December 10 (graded with feedback)
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Projects are handed in using devilry.ifi.uio.no. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via devilry.
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!eblock
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!split
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===== Topics covered in this course =====
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!bblock
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!eblock
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!split
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===== Syllabus =====
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!bblock Linear algebra and eigenvalue problems, chapters 6 and 7
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!eblock
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!split
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===== Syllabus =====
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!bblock Linear algebra and eigenvalue problems, chapters 6 and 7
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!eblock
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!split
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===== Syllabus =====
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!bblock Numerical integration, standard methods and Monte Carlo methods (chapters 4 and 11)
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!eblock
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!split
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===== Syllabus =====
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!bblock Monte Carlo methods in physics (chapters 12, 13, and 14)
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!eblock
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===== Syllabus =====
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!bblock Ordinary differential equations (chapters 8 and 9)
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!eblock
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!split
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===== Syllabus =====
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!bblock Partial differential equations, chapter 10
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!eblock
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!split
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===== Overarching aims of this course =====
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!split
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===== Additional learning outcomes =====
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!bblock
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!eblock
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===== Extremely useful tools, strongly recommended =====
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!bblock and discussed at the lab sessions
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* GIT for version control (see webpage), this week
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* ipython/jupyter notebook
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* Devilry for handing in projects, next week
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!eblock
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