Title | Model Fitting and Optimization | ![]() |
Duration | 60 | |
Module | A | |
Lesson Type | Lecture | |
Focus | Technical - Foundations of AI | |
Topic | Foundations of AI |
logistic regression, model fitting, optimization, gradient descent, Newton's method, numerical stability,
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The materials of this learning event are available under CC BY-NC-SA 4.0.
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.
Duration (min) | Description | Concepts |
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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 |
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
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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
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities |