Attended: Dr.Kuh, Seyyed, Brieanna, Jaimie, Gordon, Masaki

• Proposal for presentation:
  • Understanding data
    • Mean vectors
    • Covariance matrices
    • Representation of data
    • Sampling/filtering
  • Machine learning algorithms
    • Feature extraction
    • Clustering
    • Principle component analysis (PCA)
  • Linear regression
    • Least squares' algorithm
  • Online learning
    • Recursive least squares (RLS)
    • Least mean squares (LMS)
  • Tools for learning
    • Validation
    • Regularization
  • Getting working, documented code in Python
  • Calibration of data
  • Interface data to UHM dashboard
• Introduction to tap filters and correlation matrices

• none

• none

Contributing authors:

Created by gli on 2016/09/08 03:57.