Beskjeder

Dear all, welcome back to FYS-STK3155/4155 and a new exciting week! Our plans this week are

to discuss the family of gradient descent methods which we need to implement in the project (parts c-e, including Lasso regression).

1. Plain gradient descent (constant learning rate), reminder from last week with examples using OLS and Ridge

2. Improving gradient descent with momentum

3. Introducing stochastic gradient descent

4. More advanced updates of the learning rate: ADAgrad, RMSprop and ADAM

Readings and Videos:

1. Recommended: The textbook Goodfellow et al, Deep Learning, contains a good introduction to gradient descent, see sections 4.3-4.5 at https://www.deeplearningbook.org/contents/nume...

Dear all, project 1 (first version) is available from the folder https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects/2025/Project1 (either as pdf+latex or jupyter-notebook) and from the jupyter-book site https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/project1.html

We will discuss the project during the coming lab sessions.

Do not hesitate to ask in case you are in doubt.

Best wishes to you all.

Dear all, welcome back to a new week. We hope you've had a nice and relaxing weekend. 

Here are the plans for the coming week. 

We will discuss in more detail the mathematics of ordinary least squares, Ridge regression and Lasso regression, introduced at the end of the lecture last week. This is will be the material for the first lecture on Monday. Thereafter (second lecture) we will start discussing the numerical solution of the optimization problem using gradient methods, or what are normally called gradient descent methods. 

Ordinary Least Squares and Ridge regression are methods where we have an analytical solution for the optimal parameters. For Lasso regression we need to solve the equations numerically. This is the standard situation in essentially all machine learning methods.  Introducing gradient methods, we will thus also introduce the recipe for solving the equation for the Lasso regression...

Dear all, welcome back to FYS-STK3155/4155. We hope you've had a great weekend. This week we plan to continue our discussion of linear regression, with an emphasis on its derivation and links to mathematical interpretations. We plan also to start our discussion of Ridge and Lasso regression. The plans are

 Brief repetition from last week

o Discussions  of the equations for ordinary least squares (OLS)

o Discussion on how to prepare data and examples of applications of linear regression

o Mathematical interpretations of OLS

o Introduction of Ridge and Lasso regression

Reading recommendations:

1. The weekly lecture notes

2. Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra 

3. Raschka et al on preprocessing of data, relevant...

If you are interested in finding team mates for projects and exercises, please fill in the form at https://docs.google.com/forms/d/1W57mA196ojFKxqW4-T6NsN0rx1BFUl-j8b4nQzrcG34/edit

First of all a warm welcome to you all.

Our first lecture is Monday August 18, 215pm-4pm.

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 exercises 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.

Note that there is no mandatory presence. All lectures can be accessed either in person or live via zoom at  https://uio.zoom.us/my/mortenhj

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