Plans for week 40, September 29-October 3

Dear all, we hope you've had a great weekend.

Here are  the updates and plans for this and the coming week.

Today we will continue our discussion of logistic regression that we started last week, with coding examples as well. We will repeat some of the essential elements and derivations. Logistic regression will serve as our stepping stone towards neural networks and deep learning methods. Next week we will devote our time to setting up a neural network code and we will also introduce automatic differentiation, which will allow us to compute gradients and derivatives for different cost functions, without having to encode directly the expressions for the derivatives.

The plans for this week, with some video recommendations, are:

Lecture Monday September 29, 2025

1. Logistic regression and gradient descent, examples on how to code

2. 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)

2. For neural networks we recommend Goodfellow et al chapter 6 and Raschka et al chapter 2 (contains also material about gradient descent) and chapter 11 (we will use this next week)

3. Neural Networks demystified at https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs

4. Building Neural Networks from scratch at URL:https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex"

Lab sessions Tuesday and Wednesday

Material for the active learning sessions on Tuesday and Wednesday.

• Work on project 1 and discussions on how to structure your report

• No weekly exercises for week 40, project work only

• Video on how to write scientific reports recorded during one of the lab sessions at https://youtu.be/tVW1ZDmZnwM

• A general guideline can be found at https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md.

 Best wishes to you all,

Morten et al.