2. Trinity College Dublin, The University of Dublin
Overview previous lecture
2
• Module outline and schedule
• What is AI
• What is ML
• Supervised vs. Unsupervised learning
• Any questions?
About 25-30 contact hours out of 125 hours in total
How should you spend that time?
This module is designed for you!
Lecture
Theory + case
studies
Technical, tutorials
3. Trinity College Dublin, The University of Dublin 3
Anaconda distribution
It installs many packages (libraries and applications) that are useful for ML
4. Trinity College Dublin, The University of Dublin 4
https://jupyter-notebook.readthedocs.io/en/stable/
https://jupyter.readthedocs.io/en/latest/install/notebook-classic.html
https://anaconda.cloud/tutorials/getting-started-with-anaconda-individual-edition?source=win_installer
Setting up your coding environment
- Windows or Mac OS: run Anaconda Navigator from
the Start menu or application menu
- In Linux: run anaconda-navigator from the terminal
5. Trinity College Dublin, The University of Dublin 5
Problem/question Data collection
Preprocessing /
cleaning
Analysing
Interpretation /
outcome
Improve
ML
End-to-end ML pipeline
Visualisation Visualisation Visualisation
6. Trinity College Dublin, The University of Dublin 6
Real-world challenge
Oliveira F. et al., Survey of Technologies and Recent
Developments for Sustainable Smart Cycling. Sustainability 2021
- Predict number of bike-shares for a particular station – when does the
company need to move the bikes?
Monday, 7-9 am
Blue: losing bikes
Red: Gaining bikes
7. Trinity College Dublin, The University of Dublin 7
Real-world challenge
Oliveira F. et al., Survey of Technologies and Recent
Developments for Sustainable Smart Cycling. Sustainability 2021
- Predict number of bike-shares for a particular station – when does the
company need to move the bikes?
Friday, 1-2:30 pm
Blue: losing bikes
Red: Gaining bikes
8. Trinity College Dublin, The University of Dublin 8
Real-world challenge
Oliveira F. et al., Survey of Technologies and Recent
Developments for Sustainable Smart Cycling. Sustainability 2021
- Predict number of bike-shares for a particular station – when does the
company need to move the bikes?
Scenario A
Scenario B
Past Future (prediction)
Past Future (prediction)
A fixed schedule doesn’t work.. There are too many
changing factors (e.g., roadwork, accidents, weather)
9. Trinity College Dublin, The University of Dublin 9
Real-world challenge
Oliveira F. et al., Survey of Technologies and Recent
Developments for Sustainable Smart Cycling. Sustainability 2021
- Predict number of bike-shares for a particular station – when does the
company need to move the bikes?
- Identify anomalies (a bike is not used for a long time.. maybe it is damaged)
- Predict anomalies (anticipate problems and repair bike)
- Similar challenge for traffic management and other goals for smart cities
10. Trinity College Dublin, The University of Dublin 10
Problem/question Data collection
Preprocessing /
cleaning
Analysing
Interpretation /
outcome
Improve
ML
End-to-end ML pipeline
Visualisation Visualisation Visualisation
11. Trinity College Dublin, The University of Dublin 11
Internet of Things
Oliveira F. et al., Survey of Technologies and Recent
Developments for Sustainable Smart Cycling. Sustainability 2021 IoT: Internet of Things: Network of physical objects (e.g., sensors)
12. Trinity College Dublin, The University of Dublin 12
Internet of Things
Oliveira F. et al., Survey of Technologies and Recent
Developments for Sustainable Smart Cycling. Sustainability 2021 IoT: Internet of Things: Network of physical objects (e.g., sensors)
14. Trinity College Dublin, The University of Dublin 14
City Bikes in Smart Cities
Oliveira F. et al., Survey of Technologies and Recent
Developments for Sustainable Smart Cycling. Sustainability 2021
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Problem/question Data collection
Preprocessing /
cleaning
Analysing
Interpretation /
outcome
Improve
ML
End-to-end ML pipeline
Visualisation Visualisation Visualisation
ML challenge – many approaches (we’ll look into a few)
- Technological challenge: How do we
collect that data?
- Privacy and data protection challenges
(e.g., how do we store the data? Will
people be ok with that?)
17. Trinity College Dublin, The University of Dublin 17
Problem/question Data collection
Preprocessing /
cleaning
Analysing
Interpretation /
outcome
Improve
ML
End-to-end ML pipeline
Visualisation Visualisation Visualisation
Communication
How does the ML expert think?
