Teaching

Spring 2024
Machine Learning for Signal Processing (ENGR-E 511, CSCI-B 590)

This course focuses on the mathematical foundations of machine learning with applications-oriented assignments in signal processing.

Topics discussed:

  • Machine learning topics
    • Probability Theory (Binomial, Multinomail, Normal, Poisson distributions, MLE, Bayes)
    • Linear Algebra for ML (matrix decomposition, vector calculus)
    • Numerical Optimization (gradient descent, Newton’s method, automatic differentiation)
    • Dimensionality reduction
    • Clustering
    • Bayesian classification
    • Undirected graphical models (Markov random fields, variational inference, Mean-field approximation)
    • Nonlinear methods (spectral clustering, kernel methods)
    • Neural networks
    • Hidden Markov models
    • Support vector machines
    • Probabilistic topic modelling
    • Adaptive methods
  • Signal processing topics
    • Fourier transformation
    • Audio denoising
    • Souce seperation
    • Stereo matching
    • Image segmentation
    • Keyword detection
    • Brain signal processing
Prior Teaching Experiences (Associate Instructor or Co-lecturing)
  • Fall 2022, Spring 2023 : Deep Learning Systems - ENGR 533 (AI)
  • Spring 2022, Spring 2021 : Cyber-Physical Systems - ENGR 210 (AI)
  • Fall 2021, Advanced Cyber-Physical Systems - ENGR-321 (AI + Co-conducted lectures)
  • Spring 2019, Internet of Things - ENGR-523 (AI)
  • Fall 2018, Machine Learning for Signal Processing - ENGR 511 (AI)