MACHINE LEARNING
Machine LearningÂ
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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 HTML Keras PyTorch PDF PowerPoints Problem Sets Solution
Installation colab installation
Python Basics of Python
Linear Algebra iNote#01 iKeras#01 pdf#01 pptx#01 PS#01 PS#01 Sol'n
Optimization iNote#02 iKeras#02 pdf#02 pptx#02 PS#02 PS#02 Sol'n
Regression iNote#03 iKeras#03 pdf#03 pptx#03 PS#03 PS#03 Sol'n
Classification iNote#04 iKeras#04 pdf#04 pptx#04 PS#04 PS#04 Sol'n
kNN, Decision Tree iNote#05 iKeras#05 pdf#05 pptx#05 PS#05 PS#05 Sol'n
Clustering: K-means iNote#06 iKeras#06 pdf#06 pptx#06 PS#06 PS#06 Sol'n
Dimension Reduction iNote#07 iKeras#07 pdf#07 pptx#07 PS#07 PS#07 Sol'n
Midterm Part I, Part II
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Artificial Neural Networks (ANN) iNote#08 iKeras#08 iTorch#08 pdf#08 pptx#08 PS#08 PS#08 Sol'n
Autoencoder iNote#09 iKeras#09 iTorch#09 pdf#09 pptx#09 PS#09 PS#09 Sol'n
Convolutional Neural Networks (CNN) iNote#10 iKeras#10 iTorch#10 pdf#10 pptx#10 PS#10 PS#10 Sol'n
Recurrent Neural Networks (RNN) iNote#11 iKeras#11 iTorch#11 pdf#11 pptx#11 PS#11 PS#11 Sol'n
Physics-informed Neural Networks (PINN) iNote#12 iKeras#12 iTorch#12 pdf#12 pptx#12 PS#12 PS#12 Sol'n
AI in ME: Fluid Mechanics iNote#13 iKeras#13 iTorch#13 pdf#13 pptx#13 iYouTube#13
AI in ME: Manufacturing iNote#14 iKeras#14 iTorch#14 pdf#14 pptx#14 iYouTube#14
AI in ME: Heat Transfer iNote#15 iKeras#15 iTorch#15 pdf#15 pptx#15 iYouTube#15
AI in ME: Solid Mechanics iNote#16 iKeras#16 iTorch#16 pdf#16 pptx#16 iYouTube#16
AI in ME: Dynamics iNote#17 iKeras#17 iTorch#17 pdf#17 pptx#17
Final Exam Part I, Part II
Advanced Machine Learning
Feel free to use, modify, and distribute as needed
Topics Python Colab Slides PowerPoints
Independent Component Analysis (ICA) iNote#22 iColab#22 pdf#22 pptx#22
Singular Value Decomposition (SVD) iNote#23 iColab#23 pdf#23 pptx#23
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
Gaussian Process iNote#28 iColab#28 pdf#28 pptx#28
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