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Morten Hjorth-Jensen
2023-08-21 13:40:37 +02:00
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doc/src/week34/beamerweek34.do.txt
+31 -11
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@@ -1,8 +1,6 @@
TITLE: Week 34: Introduction to the course, Logistics and Practicalities
AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics and Center for Computing in Science Education, University of Oslo, Norway & Department of Physics and Astronomy and Facility for Rare Isotope Beams, Michigan State University, USA
DATE: Week 34, August 21-25, 2021
DATE: Week 34, August 21-25, 2023
@@ -53,7 +51,7 @@ The labs are also available till 6pm Tuesdays and Wednesdays. Videos and learnin
===== Communication channels =====
* Chat and communications via URL:"canvas.uio.no"
* _Slack_ channel: machinelearninguio.slack.com. Will most likely change.
* _Discord_ channel will be added asap
!split
@@ -80,7 +78,7 @@ The labs are also available till 6pm Tuesdays and Wednesdays. Videos and learnin
* _Office_: Department of Physics, University of Oslo, Eastern wing, room FØ470
* _Office hours_: *Anytime*! Individual or group office hours can be arranged either in person or via zoom. Feel free to send an email for planning.
* Ida Torkjellsdatter Storehaug, i.t.storehaug@fys.uio.no
* Fahimeh Najafi, fahim.n.20@gmail.com
* Fahimeh Najafi, fahimeh.najafi@fys.uio.no
* Mia-Katrin Ose Kvalsund, m.k.o.kvalsund@fys.uio.no
* Karl Henrik Fredly, k.h.fredly@fys.uio.no
* Adam Jakobsen, adam.jakobsen@fys.uio.no
@@ -98,9 +96,26 @@ o Project 3: December 11 (available November 10, graded with feedback)
!bblock
Extra Credit (not mandatory), weekly exercise assignments, 10 in total (due Friday same week), 20% additional score. The extra credit assignments are due each Friday and can be uploaed to _Canvas_ in your preferred format (although we prefer jupyter-notebooks). First assignment is for week 35.
Extra Credit (not mandatory), weekly exercise assignments, 10 in total (due Friday same week), 10% additional score. The extra credit assignments are due each Friday and can be uploaed to _Canvas_ in your preferred format (although we prefer jupyter-notebooks). First assignment is for week 35. Each weekly exercise set counts 1%.
!eblock
!split
===== Grading =====
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 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
In addition you can get an extra 10% score for weekly assignments (10 in total and due each Friday). Each weekly assignment counts 1%.
!split
===== Reading material =====
@@ -223,10 +238,12 @@ Hands-on demonstrations, exercises and projects aim at deepening your understand
!split
===== Learning outcomes =====
!bblock
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,
@@ -238,13 +255,15 @@ 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
ethical conduct is emphasized throughout the course.
!eblock
!split
===== More on learning outcomes =====
===== Learning outcomes, continued =====
More
specifically, after this course you will
!bblock
* 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;
@@ -255,5 +274,6 @@ specifically, after this course you will
* 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.
!eblock
doc/src/week34/week34.do.txt
+23 -4
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@@ -1,6 +1,6 @@
TITLE: Week 34: Introduction to the course, Logistics and Practicalities
AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics and Center for Computing in Science Education, University of Oslo, Norway & Department of Physics and Astronomy and Facility for Rare Isotope Beams, Michigan State University, USA
DATE: Week 34, August 21-25, 2021
DATE: Week 34, August 21-25, 2023
@@ -51,7 +51,7 @@ The labs are also available till 6pm Tuesdays and Wednesdays. Videos and learnin
===== Communication channels =====
* Chat and communications via URL:"canvas.uio.no"
* _Slack_ channel: machinelearninguio.slack.com. Will most likely change.
* _Discord_ channel will be added asap
!split
@@ -78,7 +78,7 @@ The labs are also available till 6pm Tuesdays and Wednesdays. Videos and learnin
* _Office_: Department of Physics, University of Oslo, Eastern wing, room FØ470
* _Office hours_: *Anytime*! Individual or group office hours can be arranged either in person or via zoom. Feel free to send an email for planning.
* Ida Torkjellsdatter Storehaug, i.t.storehaug@fys.uio.no
* Fahimeh Najafi, fahim.n.20@gmail.com
* Fahimeh Najafi, fahimeh.najafi@fys.uio.no
* Mia-Katrin Ose Kvalsund, m.k.o.kvalsund@fys.uio.no
* Karl Henrik Fredly, k.h.fredly@fys.uio.no
* Adam Jakobsen, adam.jakobsen@fys.uio.no
@@ -96,9 +96,26 @@ o Project 3: December 11 (available November 10, graded with feedback)
!bblock
Extra Credit (not mandatory), weekly exercise assignments, 10 in total (due Friday same week), 20% additional score. The extra credit assignments are due each Friday and can be uploaed to _Canvas_ in your preferred format (although we prefer jupyter-notebooks). First assignment is for week 35.
Extra Credit (not mandatory), weekly exercise assignments, 10 in total (due Friday same week), 10% additional score. The extra credit assignments are due each Friday and can be uploaed to _Canvas_ in your preferred format (although we prefer jupyter-notebooks). First assignment is for week 35. Each weekly exercise set counts 1%.
!eblock
!split
===== Grading =====
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 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
In addition you can get an extra 10% score for weekly assignments (10 in total and due each Friday). Each weekly assignment counts 1%.
!split
===== Reading material =====
@@ -2622,3 +2639,5 @@ Add now a model which allows you to make polynomials up to degree $15$. Perform