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

Learning Events to be Completed Before

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

Machine Learning Basics Lecture 6: Overfitting (princeton.edu)
Regularization - Colaboratory (google.com)
Underfit-Overfit - Colaboratory (google.com)

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