Course Highlights
  • Design experiments that will collect good quality data for use in machine learning and deep learning models
  • Improve and organise datasets collected experimentally for use in machine learning and deep learning models
  • Compare and select machine learning and deep learning models for use with your experimental data
  • Interpret and evaluate results from machine learning and deep learning models and discuss ethical considerations surrounding their use
  • Report effectively on findings from machine learning and deep learning models trained on experimental data
Curriculum

3 Topics
Welcome to the course
Data collection and annotation
Summary and review

4 Topics
Organising your datasets
Expanding your dataset
Releasing data
Summary and review

3 Topics
Software model selection and training
Improving performance
Summary and Review

4 Topics
Trusting results
Interpreting output
Improving results
Summary and review

2 Topics
Tips and tricks
Course summary

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Experimental Design for Machine Learning

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