Scientific and physics-aware machine learning, and data assimilation
  • Luca Magri
    • Group
    • Collaborations
  • Publications
  • Research
    • Overview
    • Scientific machine learning >
      • Physics-aware machine learning
      • Chaotic time series forecasting
      • Nonlinear model reduction
      • Super-resolution and reconstruction
    • Real-time digital twins and data assimilation >
      • Inferring unknown unknowns: Bias-aware data assimilation
    • Optimization >
      • Bayesian optimisation
      • Chaotic systems
    • Mathematical modelling of multi-physics fluids >
      • Reacting flows and sound
    • Quantum computing and machine learning >
      • Solving nonlinear equations with quantum algorithms
      • Linear methods from quantum mechanics
    • Data and codes
  • Jobs/grants
  • Outreach
    • Research Centre in Data-Driven Engineering
    • Data-driven methods, machine learning and optimization
    • Data-driven Dynamical Systems Analysis
  • Consultancy
  • Teaching
    • University modules
    • Artificial intelligence for engineering
    • Mathematical methods
    • Misc
  • Contact

Artificial intelligence for engineering

Lecture notes (handouts, codes and Jupyter notebookes)

1. Introduction to machine learning, data science, artificial intelligence. What are they? Why in engineering?
2. Linear regression
3. Logistic regression (classification)
4. Support vector machines
5. Feedforward neural networks
6. Convolutional neural networks
7. Recurrent neural networks and sequence modelling
8. Unsupervised learning 

Download: Syllabus

Handouts, Jupyter codes, and slides are available on BlackBoard (restrict access)

Videos

Computational graphs and feedforward neural networks 


Forward propagation and backpropagation
Convolutional neural networks
Unsupervised learning
© 2024 Luca Magri
  • Luca Magri
    • Group
    • Collaborations
  • Publications
  • Research
    • Overview
    • Scientific machine learning >
      • Physics-aware machine learning
      • Chaotic time series forecasting
      • Nonlinear model reduction
      • Super-resolution and reconstruction
    • Real-time digital twins and data assimilation >
      • Inferring unknown unknowns: Bias-aware data assimilation
    • Optimization >
      • Bayesian optimisation
      • Chaotic systems
    • Mathematical modelling of multi-physics fluids >
      • Reacting flows and sound
    • Quantum computing and machine learning >
      • Solving nonlinear equations with quantum algorithms
      • Linear methods from quantum mechanics
    • Data and codes
  • Jobs/grants
  • Outreach
    • Research Centre in Data-Driven Engineering
    • Data-driven methods, machine learning and optimization
    • Data-driven Dynamical Systems Analysis
  • Consultancy
  • Teaching
    • University modules
    • Artificial intelligence for engineering
    • Mathematical methods
    • Misc
  • Contact