Practical: Model Fitting and Optimization

Practical: Model Fitting and Optimization

Administrative information

Title Model Fitting and Optimization
Duration 150-180 min
Module A
Lesson Type Practical
Focus Technical - Foundations of AI
Topic Fitting and Optimization

 

Keywords

model fitting,optimization,binary classification,regression,

 

Learning Goals

  • Visualise and scale the features and labels to simply the classification problem.
  • Use the metrics to evaluate the classification model.
  • Tune the hyperparameters to improve the model performance.

 

Expected Preparation

Obligatory for Students

  • Students should have hands-on experience in python programming
  • Students should have good understanding of Data exploration techniques
  • Students should have reviewed lectures and demonstration on topics of Model Types, Model Evaluation, Model Fitting and Model Optimization

Optional for Students

None.

References and background for students:

None.

Recommended for Teachers

Lesson Materials


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

 

Instructions for Teachers

Follow the steps in the Colab.

Outline of lecture

 
Duration (min) Description Activity Material
0-15 min A brief overview of the tasks and learning goals Instructions by the lecturer colab practical link for lecturer
15 - 40 min Task 1 - Explore the dataset - Visualise and summarise the findings. Normalize and label the target variable. Reporting - investigation of data (bias, redundancy, ethical)
40 - 75 min Task 2 - Model Evaluation - Model Evaluation based on Train and Test data. Coding
75 - 105 min Task 3 - Model Optimization - Use hyperparameter tuning and modify the threshold to improve the performance. Coding
105 - 135 min Task 4 - Model Optimization - Summarise the model performance of Task 3 Reporting - Summary
135-150 min Summary of the practical Conclusion by the lecturer

 

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

Colofon

Het arrangement Practical: Model Fitting and Optimization is gemaakt met Wikiwijs van Kennisnet. Wikiwijs is hét onderwijsplatform waar je leermiddelen zoekt, maakt en deelt.

Auteur
HCAIM Consortium
Laatst gewijzigd
2024-05-15 11:06:47
Licentie

Dit lesmateriaal is gepubliceerd onder de Creative Commons Naamsvermelding-GelijkDelen 4.0 Internationale licentie. Dit houdt in dat je onder de voorwaarde van naamsvermelding en publicatie onder dezelfde licentie vrij bent om:

  • het werk te delen - te kopiëren, te verspreiden en door te geven via elk medium of bestandsformaat
  • het werk te bewerken - te remixen, te veranderen en afgeleide werken te maken
  • voor alle doeleinden, inclusief commerciële doeleinden.

Meer informatie over de CC Naamsvermelding-GelijkDelen 4.0 Internationale licentie.

Aanvullende informatie over dit lesmateriaal

Van dit lesmateriaal is de volgende aanvullende informatie beschikbaar:

Toelichting
.
Eindgebruiker
leerling/student
Moeilijkheidsgraad
gemiddeld
Studiebelasting
4 uur en 0 minuten

Gebruikte Wikiwijs Arrangementen

HCAIM Consortium. (z.d.).

Acknowledgement

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

HCAIM Consortium. (z.d.).

Lecture: Duty Ethics

https://maken.wikiwijs.nl/198966/Lecture__Duty_Ethics

close
Colofon
thumb  
gemaakt met Wikiwijs van kennisnet-logo
Practical: Model Fitting and Optimization
open