Principles of Data and Error Analysis in Engineering Measurements

(Topics in Signal Processing)
Instructor: Miodrag Bolic, University of Ottawa, Ottawa, Canada
www.site.uottawa.ca/~mbolic
Time and place: Monday 10:00 - 13:00, MNO E218
Course code: ELG 7172B (EACJ 5600)

Calendar Style Description:

Uncertainty, Uncertainty propagation, Bayesian Inference, Bayesian Filtering, Data fusion, Metrology, Measurement Science, Error Analysis, Measures of Agreement, Data Quality, Data quality index. Case studies will be drawn from various fields including biomedical instrumentation, sensors and signal processing.

Prerequisites: We expect participating students to bring basic knowledge and experience in

• Programming using Python
• Elementary Probability
• Elementary Statistics

Grading: For collecting the credits the student are expected to

• assignments (45% of the grade)+optional up 25% bonus points for small projects/literature reviews,
• scribing(15% of the grade)
• final exam (40% of the grade)

Here is some software you may find helpful for your projects:

• deep learning frameworks
  • TensorFlow (probably has the most relevant software for this course)
  • PyTorch
  • Theano
  • Autograd (lightweight autodiff framework; easier to experiment with than the other frameworks, but CPU-based)
• probabilistic programming languages
  • Stan (the most widely used PPL; uses HMC, but somewhat of a black box)
  • Edward (a PPL aimed at researchers, based on HMC and stochastic variational inference)
• Gaussian processes
  • GPy
  • GPflow
• Bayesian optimization
  • Spearmint
  • HPOLib
• reinforcement learning
  • OpenAI Gym
  • OpenAI Baselines
• adversarial robustness
  • CleverHans
  • Foolbox