MACHINE LEARNING

Machine Learning 

Dates Topics with Python Colab Slides Problem Sets Colab Solution

  Installation colab installation

  Python Basics of Python

  Introduction iNote#00 pdf#00

  Linear Algebra iNote#01 iColab#01 pdf#01 PS#01 iPS#01 HW#01 Solution

  Optimization iNote#02 iColab#02 pdf#02 PS#02 iPS#02 HW#02 Solution

  Regression: Basics iNote#03 iColab#03 pdf#03 PS#03 iPS#03 HW#03 Solution

  Regression: Overfitting and Regularization iNote#04 iColab#04

  Regression: Examples iNote#05 iColab#05 HW#04 Solution

  Classification: Perceptron iNote#06 iColab#06 pdf#04 PS#04 iPS#04 HW#05 Solution

  Classification: SVM iNote#07 iColab#07 pdf#05

  Classification: Logistic Regression iNote#08 iColab#08 pdf#06

 

Midterm Part I, Part II


  kNN iNote#09 iColab#09 pdf#07

  Decision Tree iNote#10 iColab#10 pdf#08 PS#05 iPS#05

  Clustering: K-means iNote#11 iColab#11 pdf#09 PS#06 iPS#06 HW#06 Solution

  Statistics iNote#12 iColab#12 pdf#10

  Dim. Reduction: PCA iNote#13 iColab#13 pdf#11 PS#07 iPS#07 HW#07 Solution

  Dim. Reduction: FDA iNote#14 iColab#14 pdf#12

  Dim. Reduction: SVD iNote#15 iColab#15 pdf#13 HW#08 Solution

  Artificial Neural Networks (ANN) iNote#16_1 iColab#16_1 PS#08 iPS#08 HW#09 Solution

  Artificial Neural Networks (ANN) iNote#16_2 iColab#16_2

  Artificial Neural Networks (ANN) iNote#16_3 iColab#16_3 pdf#15

  Dim. Reduction: Autoencoder iNote#17 iColab#17 pdf#16 PS#09 iPS#09 HW#10 Solution

 

Final Exam Part I, Part II

Probabilistic Machine Learning 

Dates   Topics   with Python   Slides Homework     Solution

Probability iNote#17 pdf#17

Gaussian Distribution iNote#18 pdf#18

Parameter Estimation iNote#19 pdf#19

Probabilistic Machine Learning iNote#20 pdf#20

Bayesian Machine Learning iNote#21 pdf#21

Advanced Machine Learning

Dates   Topics   with Python   Slides Homework     Solution

Independent Component Analysis (ICA) iNote#22

Singular Value Decomposition (SVD) iNote#23

Graph Theory iNote#24

Google PageRank iNote#25

Clustering: Spectral Partitioning iNote#26

Kalman Filter iNote#27

Gaussian Process iNote#28

Learning from Imbalanced Data iNote#29

Using Scikit-Learn iNote#30

Discrete Optimization iNote#31