About this Course

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Intermediate Level

Python programming and experience with basic packages such as numpy, scipy and matplotlib

Approx. 30 hours to complete
English

What you will learn

  • Program global explainability methods in time-series classification

  • Program local explainability methods for deep learning such as CAM and GRAD-CAM

  • Understand axiomatic attributions for deep learning networks

  • Incorporate attention in Recurrent Neural Networks and visualise the attention weights

Skills you will gain

  • attention mechanisms
  • explainable machine learning models
  • model-agnostic and model specific models
  • global and local explanations
  • interpretability vs explainability
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Intermediate Level

Python programming and experience with basic packages such as numpy, scipy and matplotlib

Approx. 30 hours to complete
English

Offered by

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University of Glasgow

Syllabus - What you will learn from this course

Week
1

Week 1

9 hours to complete

Interpretable vs Explainable Machine Learning Models in Healthcare

9 hours to complete
6 videos (Total 72 min), 8 readings, 1 quiz
Week
2

Week 2

8 hours to complete

Local Explainability Methods for Deep Learning Models

8 hours to complete
5 videos (Total 48 min), 7 readings, 1 quiz
Week
3

Week 3

8 hours to complete

Gradient-weighted Class Activation Mapping and Integrated Gradients

8 hours to complete
4 videos (Total 37 min), 6 readings, 1 quiz
Week
4

Week 4

5 hours to complete

Attention mechanisms in Deep Learning

5 hours to complete
3 videos (Total 34 min), 3 readings, 2 quizzes

About the Informed Clinical Decision Making using Deep Learning Specialization

Informed Clinical Decision Making using Deep Learning

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