ECOLOGICAL INFORMATICS FOR SUSTAINABLE DEVELOPMENT GOALS Environmental Policy Analytics Scalable environmental start-up initiatives Communicating Science and Informing decision Futurecasting research and education in Ecological Informatics We envisage the three themes to be both independent as well as interdependent. Note: 1- The topics listed under each theme are only indicative. The organizers will be glad to accommodate relevant topics that are not indicated at present. In addition to expanding the scope by adding new themes, we look forward to building upon and take forward theme(s) initiated in Jena. Responsibilities of session chair: As a proposer of an accepted ICEI 2020 1 session, you will become the session chair.

Everyone is wanting to learn more about how Machine Learning can be used in their business. What's interesting though is that many companies may already be using Machine Learning to some extent without really realising it. The lines between predictive analytics and Machine Learning are actually quite blurred. Many companies will have built up some Machine Learning capabilities using predictive analytics in some area of their business. So if you use static predictive models in your business, then you are already using Machine Learning, albeit of the static variety.

If you visit the data science competitions website Kaggle, the example problem that they have for new starters is predicting the survivors and non-survivors from the Titanic from a sampled training dataset. This training dataset contains various attributes of the passengers, like the passenger's ticket class, the boarding gate, number of siblings, age, gender, etc. One can build a model using whatever tool you prefer and test your algorithm on a sample and see how you faired against the best of the best. Don't be disillusioned if your model only comes out at 76% accuracy against the winner's 100%. One can just Google the actual names and see who lived and who did not.

Machine learning is ubiquitous and used to make critical business and life decisions every day. Each machine learning problem is unique, so it can be challenging to manage raw data, identify key features that impact your model, train multiple models, and perform model assessments. This session explores the fundamentals of machine learning using MATLAB . Rory reviews typical workflows for both supervised (classification and regression) and unsupervised learning, through examples. This presentation demonstrates examples of new functionality in Statistics and Machine Learning Toolbox and Neural Network Toolbox .