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Machine Learning (Graduated Course)

  • Course Number: EE 6550
  • Year: 2022-Spring

Homeworks & Final Project

  • HW1 (20%): Maximum A Posteriori probability estimator
    • Predict the classes for "Wine" dataset
  • HW2 (20%): Maximum Likelihood (ML) & Bayesian Linear Regression
    • Utilize the Gaussian basis function to form feature vector
    • The final MSE loss (validation loss < 0.01 is achieved)
  • HW3 (20%): 2-layer & 3-layer NN handcrafted implementation
    • Implement the back propagation algorithm
    • Use only numpy package to train from scratch
  • Final Project (40%): Behavior-Classification-of-Exposition-Visitors