As part of FUDIPO project, an online PhD course about Machine Learning and Process Analytics was organised by project consortium members, from 5th October to 11th December 2017. The lecturers and topics are listed next, and the presentations can be downloaded by clicking on the topic name. FUDIPO consortium hopes you find this material useful and gives you deeper knowledge in this thrilling field.
- Erik Dahlquist (MDH): Introduction to the course: How is the brain learning?
- Farzaneh Ahmadzadeh (MDH): Decission support, MCDM (Multi-Criteria Decision Makin), Compensatory Methods such as SAW, Electre, Topsis, ER, and their application
- Ioanna Aslanidou and Valentina Zaccaria (MDH): Foundations for the development of a diagnostics framework
- Jesús Zambrano (MDH): Gaussian Process Regression and Gaussian Mixture Models for data monitoring and possible applications
- Jawad Elomari and Markus Bohlin (RISE SICS): Production Planning and Optiization. Optimal power flow and unit commitment problems and nonlinear optimization techniques.
- Ather Gattami: Introduction to Reinforcement Learning
- Alejandro del Real (IDENER): Model based control
- Erik Dahlquist (MDH): Implementation of Soft Sensors
- Richard Reisinger (Tieto): Data Quality Assurance and Control
- Ning Xiong and Erik Dahlquist (MDH): Deep Learning
- Mobyen Ahmed and Shahina Begum (MDH): Learning algorithms – regressions