Lecture: Model Fitting and Optimization

Lecture: Model Fitting and Optimization

Administrative information

Title Model Fitting and Optimization
Duration 60
Module A
Lesson Type Lecture
Focus Technical - Foundations of AI
Topic Foundations of AI

 

Keywords

logistic regression, model fitting, optimization, gradient descent, Newton's method, numerical stability,

 

Learning Goals

  • To acquire demonstrable knowledge of what logistic regression is
  • To acquire demonstrable knowledge of ML inference in non-conjugate models via gradient descent
  • To acquire demonstrable knowledge on the design and implementation of gradient-based optimization algorithms

 

Expected Preparation

Learning Events to be Completed Before

Obligatory for Students

  • Review the basics of Bayesian inference and maximum likelihood
  • Review elementary vector calculus

Optional for Students

None.

References and background for students:

None.

Recommended for Teachers

  • Familiarize themselves with the demonstration material

Lesson Materials

 

The materials of this learning event are available under CC BY-NC-SA 4.0.

 

Instructions for Teachers

Cover the topics in the lesson outline and demonstrate the concepts using the interactive notebook (relationship between the number of iterations, loss value and decision boundary, show various algorithms and the effect of the learning rate). Give a brief overview of the code.

Outline/time schedule

Duration (min) Description Concepts
5 Introduction to linear classification binary classification, decision boundary
15 Defining a logistic regression model class-conditional density, sigmoid function, logistic regression
15 Maximum likelihood estimation binary crossentropy, learning rate, gradient descent
10 Implementation details and numerical stability numerical stability, overflow
10 Advanced algorithms Newton's method, line search
5 Demonstration  

 

More information

Click here for an overview of all lesson plans of the master human centred AI

Please visit the home page of the consortium HCAIM

Acknowledgements

The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.

The materials of this learning event are available under CC BY-NC-SA 4.0

 

The HCAIM consortium consists of three excellence centres, three SMEs and four Universities

HCAIM Consortium

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    HCAIM Consortium
    Laatst gewijzigd
    2024-05-15 11:03:04
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    Acknowledgement

    https://maken.wikiwijs.nl/198386/Acknowledgement

    HCAIM Consortium. (z.d.).

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