MACHINE LEARNING

Machine Learning

These lecture materials for Machine Learning are openly available to everyone.

For students: You are encouraged to use these materials to support your studies.
For instructors: You are welcome to use, modify, and distribute these materials in your teaching.

No credit or reference to us is required.

Topics Short Colab Full Colab PDF PowerPoints Problem Sets Solution

Introduction pdf pptx

Linear Algebra iColab iColab pdf pptx PS#01 PS#01 Sol'n

Optimization iColab iColab pdf pptx PS#02 PS#02 Sol'n

Regression iColab iColab pdf pptx PS#03 PS#03 Sol'n

Classification iColab iColab pdf pptx PS#04 PS#04 Sol'n

kNN, Decision Tree iColab iColab pdf pptx PS#05 PS#05 Sol'n

Clustering: K-means iColab iColab pdf pptx PS#06 PS#06 Sol'n

Dimension Reduction iColab iColab pdf pptx PS#07 PS#07 Sol'n

Midterm

Artificial Neural Networks (ANN) iColab iColab pdf pptx PS#08 PS#08 Sol'n

Autoencoder iColab iColab pdf pptx PS#09 PS#09 Sol'n

Convolutional Neural Networks (CNN) iColab iColab pdf pptx PS#10 PS#10 Sol'n

eXplainable AI (XAI) iColab iColab pdf pptx PS#11 PS#11Sol'n

Recurrent Neural Networks (RNN) iColab iColab pdf pptx PS#12 PS#12 Sol'n

Active Learning iColab iColab pdf pptx PS#13 PS#13 Sol'n

Physics-informed Neural Networks (PINN) iColab iColab pdf pptx PS#14 PS#14 Sol'n

AI in Mechanical Engineering

Final Exam

Topics HTML Colab PDF PowerPoints

Probabilistic Machine Learning iNote#24 iColab#24 pdf#24 pptx#24

Gaussian Process iNote#26 iColab#26 pdf#26 pptx#26

Topics Python Colab Slides PowerPoints

Independent Component Analysis (ICA) iNote#22 iColab#22 pdf#22 pptx#22

Graph Theory iNote#24 iColab#24 pdf#24 pptx#24

Google PageRank iNote#25 iColab#25 pdf#25 pptx#25

Clustering: Spectral Partitioning iNote#26 iColab#26 pdf#26 pptx#26

Kalman Filter iNote#27 iColab#27 pdf#27 pptx#27

Learning from Imbalanced Data iNote#29 iColab#29 pdf#29 pptx#29

Using Scikit-Learn iNote#30 iColab#30 pdf#30 pptx#30

Discrete Optimization iNote#31 iColab#31 pdf#31 pptx#31