Beskjeder

Dear all, welcome to our last week of lectures. It feels always sad when the semester is over. It has been a true pleasure to meet you all.

Our last lecture is this Monday, November 25. Note that we are going to have the lecture in Store Fysiske Auditorium, just one floor below the place where we have been having our lectures.

Here is our plan for this coming week, plus plan for the lab sessions for the week of December 2-6.

Plans for the lecture Monday 25 November, with video suggestions etc.

1. Boosting and gradient boosting and ensemble models

2. Summary of course

3. Readings and Videos:

a. These lecture notes at https://github.com/CompPhys...

Dear all, we hope your weekend has been an excellent and relaxing one. Here are our plans for the second last week (our last lecture is November 25):

Lab sessions on Tuesday and Wednesday.

• Work and Discussion of project 3

• Second last weekly exercise, see the attached exercise set or go to https://github.com/CompPhysics/MachineLearning/blob/master/doc/LectureNotes/exercisesweek47.ipynb 

Plans for the lecture Monday 18 November, with video suggestions etc.

- Basics of decision trees, classification and regression algorithms and ensemble models such as bagging, random forests and gradient boosting. The lecture will include a reminder on decision trees from last week.

...

Dear all, we hope this weekend has evolved in the best possible way and that you have all been able to recharge properly your batteries.

Again thx so much for heroic efforts with project 2. We plan to have the feedback available during the week which starts with Monday November 25.

We are now moving away from Deep learning methods and will start with a new topic (and most likely the last one this semester), namely decision trees and ensemble methods like random forests, bagging, boosting, gradient boosting and more. This week we have thus a regular lecture on Monday.

We will continue with this topic Monday November 18 and will wrap up the course on November 25 (note and error in the official teaching schedule, the lecture of Monday November 25 is missing).

Concerning the recording from Monday November 4, I need to remake the video from last week since I discovered that the sound was not optimal (too much noise)....

Dear all, you can find the relevant files for project 3 at https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects/2024/Project3

For the PDF variant go to https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects/2024/Project3/pdf

For the jupyter-notebook go to https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/2024/Project3/ipynb/Project3.ipynb

Don't hesitate to ask if something is unclear.

The tentative deadline is set to December 9. Please let us know if there are conflicts with exams and we will try to adapt the deadline.

We will have labs in the week of December 2-6 before the deadline as well.

Best wishes to you all,

Fahimeh, Ida, Karl Henrik, Mia, Morten, Odin, and Sigurd

Plans for week 45, November 4-8

Dear all, we hope all is going well with the finalization of project 2. Keep in mind that you always have one extra day to mend/correct/improve your submission. But do please uplaod your GitHub/Gitlab or similar links or files by the deadline Monday at midnight.

This coming we will start discussing project 3. The project will be available from Monday evening (November 4).

Note also that the lecture this Monday (November 4) is via zoom only, no in person lecture.  As always, the lecture will be recorded.

Plans for week 45

Material for the lecture on Monday November 4, 2024.

1. Convolutional Neural Networks, codes and examples (own code and TensorFlow and Pytorch implementations)

2. Recurrent Neural Networks (RNNs)

   ...

Dear all, welcome back to new week with FYS-STK3155/4155.

We hope you've had a great weekend.

Here are our plans for this week:

This week we start with convolutional Neural networks (we will continue with this topic next week as well).  Next week we will also discuss recurrent neural networks.

1. Suggested Readings and Videos:

   • The lecture notes at https://github.com/CompPhysics/MachineLearning/blob/master/doc/pub/week44/ipynb/week44.ipynb

   • For a more in depth discussion on neural networks we recommend Goodfellow et al chapter 9. See also chapter 11 and 12 on practicalities and applications

   • Reading suggestions for i...

Dear all, note that this coming Monday there is no in-person lecture (or direct zoom session) since Morten is away for a conference.  The video of the lecture will however be uploaded this coming Sunday.  The material is covered by the lecture notes at for example https://github.com/CompPhysics/MachineLearning/blob/master/doc/pub/week43/ipynb/week43.ipynb

The material for this week deals with how we can set up a final code for neural networks, either by writing our own code or using popular software libraries like TensorFlow and/or PyTorch.  Hopefully it can function as a source of inspiration for project 2.

The plan for this week is as follows (with literature suggestions)

Material...

Dear all, we hope your weekend turned out the best possible way.  Here are our plans for the coming week:

Lecture October 14, 2024

1. Building our own Feed-forward Neural Network and discussion of project 2

Readings and videos.

1. These lecture notes at https://github.com/CompPhysics/MachineLearning/blob/master/doc/pub/week42/ipynb/week42.ipynb

2. For a more in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7.

3. Neural Networks demystified at https://www.youtube.com/w...

Dear all, project 2 is now available at our GitHub repository, see https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects/2024/Project2

We will discuss the project during all coming lab sessions. The tentative deadline is November 4. 

Best wishes to you all,

Fahimeh, Ida, Karl Henrik, Mia, Morten, Odin, and Sigurd

Dear all, welcome back to a new week with FYS-STK3155/4155. We hope the finalization of the first project is going well.  See also the email we sent about the final stages of project 1.

The plans for this week include

Lecture on Monday October 7, 2024

1. Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.

2. Building our own Feed-forward Neural Network

3. Project 2 is available Monday evening and will be discussed during the lab sessions

Readings and Videos:

1. Lecture notes at https://github.com/CompPhysics/...

Dear all, we hope you have passed a great weekend! Here are our tentative plans for week 40:

Plans for week 40

Lecture Monday September 30, 2024

1. Stochastic Gradient descent with examples and automatic differentiation. Continuation of the discussions from last week

2. If we get time, we start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model

Suggested readings and videos

Readings and Videos:

1. The lecture notes for week 40 (these notes), see for example the jupyter-notebook at https://gi...

Dear all and welcome to a new week with machine learning and much more. 

This week we continue our discussions of logistic regression as our first encounter on classification methods. As we discussed during the lecture last week, we use logistic regression in order to introduce classification problems as well as gradient methods in order to find the optimal parameters of our model. 

Important note:  Our lectures from Monday September 23 and for the rest of the semester will be at Store Fysiske Lesesal, where we met the first time. The audio equipment has now been installed.  

The plan this week (and we will continue with these topics next week as well) is:

Lecture Monday September 23

Material for the lecture on Monday September 23.

• Repetition of Logistic regression equations and clas...

Dear all, welcome back to a new exciting week with machine learning! Our plans this week are as follows, with lecture notes at for example https://github.com/CompPhysics/MachineLearning/blob/master/doc/pub/week38/ipynb/week38.ipynb:

Material for the lecture on Monday September 16.

• Logistic regression as our first encounter of classification methods. From binary cases to several categories.

• Start gradient and optimization methods

Suggested reading and videos

• Readings and Videos:

  • Hastie et al 4.1, 4.2 and 4.3 on logistic regression

  • Raschka et al, pages 53-76 on Logistic regression a...

For those interested, here's an interesting link to the Norwegian Mapping authority (Kartverket) from Jacob Hay (text in Norwegian).

////.  Mail from Jacob Hay:

Tenkte jeg skulle nevne hoydedata.no, hvor man kan laste ned DTM / DOM fra hele norge. (Dette er en gratis tjeneste fra kartverket) Man kan her laste ned data med opp til 1m oppl?sning, samt den kontrasten av overflate-modell eller terreng-modell. (aka. med eller uten tr?r og lignende st?y.) Bestilling av kartutsnitt skal v?re gratis for alle i Norge, men det kan ta noen timer ? prosessere f?r man f?r epost med nedlastingslenke. Finnes ogs? en del alternativer p? data.norge.no https://data.norge.no/search-all?q=dtm&theme=REGI

Dear all and welcome back to FYS-STK3155/4155. 

We hope you all had a great weekend. The plans for this week focus on

1) The lab sessions

    a) Discussion of expectation values, see also lecture material for this week. The exercises this week focus on this topics and can all be reused in project 1. Take also a look at Wessel van Wieringen's article at https://arxiv.org/abs/1509.09169. This is a good read if you are somewhat rusty on expectation values and more.

    b) Else, we will focus on work on project 1

2)  Lecture on Monday September 9

  a) we will focus on a  statistical interpretation of Ridge and Lasso regression

  b) and we will start discussing resampling techniques,such as the Bootstrap and cross validation and the magic of...

Dear all, project 1 is now available at https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects/2024/Project1

You will find the project files in different formats (all with the same content obviously), from plain html, via jupyter-notebook to PDF and latex.

We will discuss the project together with various exercises during the lab sessions.  Note well that the weekly exercises are aligned with the project and can be used for extra credits.

best wishes to you all and never hesitate to ask questions during the lab sessions.

Fahimeh, Ida, Karl Henrik, Mia, Morten, Odin, and Sigurd

Dear all,  here are the plans for the coming week:

• Material for the lecture on Monday September 2
•      Linear Regression, Ridge and Lasso regression and links with Statistics, Resampling methods, see the weekly lecture slides at for example (jupyter-notebook) https://github.com/CompPhysics/MachineLearning/blob/master/doc/pub/week36/ipynb/week36.ipynb
•      Recommended Reading: Goodfellow et al chapter 3  (till 3.11) on probability theory, see https://www.deeplearningbook.org/
•    Raschka et al, chapter 4 pages 105-134 and chapter 6 pages 171-185. Chapter 4 and 6 contain many useful hints which will be relevant for the various projects as well.

• Material for the active learning sessions on Tuesday and Wednesday
•      Summary from last week on discussion of SVD,...

Free access to textbook of Raschka et al

The textbook (highly recommended) Machine Learning with PyTorch and Scikit-Learn
by Sebastian Raschka, Yuxi (Hayden) Liu, Dr. Vahid Mirjalili can be downloaded for free (pdf and epub) from the university library of Oslo, go to 
https://bibsys-almaprimo.hosted.exlibrisgroup.com/primo-explore/search?vid=UIOLinks to an external site. and search for the text and log in with your UiO credentials.

Cheers and best

Dear all, first of all we hope you had an excellent weekend. we look much forward to welcome you back to this week's exercises and lectures. 

Important note: since the lecture hall we have been assigned for the Monday sessions is not yet ready with all AV equipment, we will use our back-up auditorium in the chemistry building, Auditorium 2 till approximately mid September. We are very sorry for this and hope it won't cause too many problems. 

This week we will discuss and work on the exercises for week 35 (all relevant for the start of project 1 next week).  The material needed for these exercises is covered by the first part of the weekly slides for week 35 at  https://compphysics.github.io/MachineLearning/doc/pub/week35/html/._week35-bs041.html

The slides 1-41 contain also several examples and derivations relevant for solving the three exercises we will work on this week d...

Dear all, if you are looking after team mates and would like us to make suggestions, please feel free to fill out the form at https://docs.google.com/forms/d/e/1FAIpQLSdmqZf4zQemIF03-TBJMr_ZB78aCbiAaPA6Szrt69_5BTcdQA/viewform?pli=1&edit_requested=true&pli=1 before the end of week 34 (the week of August 19-23).  We will come with suggestions by the end of the coming weekend.

Best wishes from all of us,
Fahimeh, Ida, Karl Henrik, Mia, Morten, Odin and Sigurd

The discord channel for fall 2024 is at https://discord.gg/XBKjd4ccGq

Dear all, lectures will be recorded and posted afterwards. The zoom link is Topic: FYS-STK3155/4155 lectures

https://msu.zoom.us/j/91706435521?pwd=Zll6dU1lRVpEbmlMWU9za1dyT0gvQT09

Meeting ID: 917 0643 5521
Passcode: 220382

Overview of first week

First of all a warm welcome to you all.

Our first lecture is Monday August 19, 1015am-12pm.

The sessions on Tuesdays and Wednesdays last four hours for each group (four in total) and will include lectures in a flipped mode (promoting active learning) and work on exercices and projects. The sessions will begin with lectures and questions and answers about the material to be covered every week. There are four groups, Tuesdays 815am-12pm and 1215pm-4pm and Wednesdays 815am-12pm and 1215pm-4pm. Please sign up as soon as possible for one of the groups. Max capacity per group is 30-40 participants. Please select the group which fits you best.

The first week we start with simple linear regression, a repetition of linear algebra and elements of statistics needed for the course.

• August 19: Presentation of the course, aims and content. Introduct...