Weekly update for week 36
Dear all, first a great thank you for having chosen FYS-STK3155/4155.
What follows is a lightly longer mail, with weekly updates and information about the computational labs.
Plus other information.
Concerning the exercises and projects, for those of you searching for lab partners, please fill in your information at https://docs.google.com/forms/d/12VNXJOqMfLGism580eBps_M7zk-gzXe7Qd-B2Ll_s8o/edit
Based on your responses we will by the end of this week come back with group proposals.
Else, for our computational labs and exercise and project sessions, we have at our disposal rooms F?397 and F?434 at the Physics Building (eastern wing) from 8am till 6pm every Wednesdays (two small exceptions from 2pm in September for F?434).
The schedule is as follows
Room F?397
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8-10am: Selfstudy, the lab is open but no lab group. Feel free to come if you wish.
10-12pm: Computational Lab group 3
12-2pm: Computational Lab group 1
2-4pm: Computational Lab group 6
4-6pm: Selfstudy, the lab is open but no lab group. Feel free to come if you wish.
Room F?434
8-10am : Computational Lab group 4
10-12pm: Computational Lab group 5
12-2pm: Computational Lab group 8
2-6pm: Selfstudy, the lab is open but no lab group. Feel free to come if you wish.
Digital lab, same zoom link for the whole semester
8-10am: Digital lab group 2
2-4pm: Digital lab group 7
Topic: Computer Lab FYS-STK3155/4155
Time: This is a recurring meeting Meet anytime
Join Zoom Meeting
https://msu.zoom.us/j/95317649875?pwd=aWM1akppam4yWVBIY29KaXE5cHpSZz09
Meeting ID: 953 1764 9875
Passcode: 536396
Last week we discussed the basics of ML, the setup of the course and where to find the information. Lectures are recorded and we have also live sessions via zoom for those of you who cannot attend. The handwritten notes (which correspond to what would have been written on a blackboard) are available from the GitHub link of the course https://github.com/CompPhysics/MachineLearning at
https://github.com/CompPhysics/MachineLearning/tree/master/doc/HandWrittenNotes/2021
The exercises for this week to be discussed at the lab are at end of the weekly slides, see for example https://compphysics.github.io/MachineLearning/doc/pub/week34/html/week34.html. This coming lab session we will work on these exercises (three in total). They will, together with those for next week, serve as a background for the first project. This coming lab session we will also make sure that ll of you have Python and important libraries properly installed.
This week's lectures will go more into the mathematics of ordinary least squares with several examples. We will also discuss Ridge and Lasso regression and the links between these methods and the relevant mathematics.
The lecture notes are at https://compphysics.github.io/MachineLearning/doc/web/course.html, see the slides for week 36. Note that these notes will be updated today and their final version will be available towards the end of the day.
You can also find all the teaching material as a jupyter book at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html
The relevant material are chapters 3 and 5 on Linear regression and Ridge and Lasso regression.
The reading assignments and weekly schedule can also be found there. The recommended textbooks are at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/textbooks.html and for this week, if you wish to read more about the topics to come besides the lecture notes, we recommend
Reading recommendations: See lecture notes for week 36 at https://compphysics.github.io/MachineLearning/doc/web/course.html. HTF chapter 3. GBC chapters 1 and and sections 3.1-3.11 and 5.1 and CMB sections 1.1 and 3.1 where the acronyms are
GBC: Goodfellow, Bengio, and Courville, Deep Learning
CMB: Christopher M. Bishop, Pattern Recognition and Machine Learning
HTF: Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning
AG: Aurelien Geron, Hands?On Machine Learning with Scikit?Learn and TensorFlow
We are going to repeat many of these things (lot of information!). this week also. Please do not hesitate to ask if something is unclear.
Else, fro those of you who are not intimidated by GDPR, we have also a slack link and a piazza link at
Slack channel: machinelearninguio.slack.com
Piazza : enlist at https:piazza.com/uio.no/fall2021/fysstk4155
As default for communication we will however use mail and chat via canvas.
Best wishes to you all and see you soon,
Morten