AI for ME
AI for Mechanical Engineering
Students are anticipated to acquire knowledge of machine learning and deep learning algorithms used in data analytics and their practical implementations in Python. Although mathematical techniques and theoretical aspects will be included, the principal objective is to furnish students with the skills and fundamental principles essential for addressing data-related challenges encountered within the realm of mechanical engineering. The majority of the illustrative instances will be directly relevant to mechanical engineering.
Topics HTML Colab PDF PPTX Problem Sets Colab
Installation colab installation
Python Basics of Python
Introduction iNote#00 pdf#00 pptx#00
Linear Algebra iNote#01 iColab#01 pdf#01 pptx#01 PS#01 iPS#01
Optimization iNote#02 iColab#02 pdf#02 pptx#02 PS#02 iPS#02
Regression iNote#03 iColab#03 pdf#03 pptx#03 PS#03 iPS#03
Classification iNote#04 iColab#04 pdf#04 pptx#04 PS#04 iPS#04
kNN, Decision Tree iNote#05 iColab#05 pdf#05 pptx#05 PS#05 iPS#05
Clustering: K-means iNote#06 iColab#06 pdf#06 pptx#06 PS#06 iPS#06
Dim. Reduction iNote#07 iColab#07 pdf#07 pptx#07 PS#07 iPS#07
Midterm Part II Part II
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Artificial Neural Networks (ANN) iNote#08 iColab#08 pdf#08 pptx#08 PS#08 iPS#08
Autoencoder iNote#09 iColab#09 pdf#09 pptx#09 PS#09 iPS#09
Convolutional Neural Networks (CNN) iNote#10 iColab#10 pdf#10 pptx#10 PS#10 iPS#10
Recurrent Neural Networks (RNN) iNote#11 iColab#11 pdf#11 pptx#11 PS#11 iPS#11
eXplainable AI (XAI) iNote#12 iColab#12 pdf#12 pptx#12 PS#12 iPS#12
Transfer Learning iNote#13 iColab#13 pdf#13 pptx#13 PS#13 iPS#13
Fully Convolutional Networks (FCN) iNote#14 iColab#14 pdf#14 pptx#14 PS#14 iPS#14
Generative Adversarial Networks (GAN) iNote#15 iColab#15 pdf#15 pptx#15 PS#15 iPS#15
Transformer iNote#16 iColab#16 pdf#16 pptx#16 PS#16 iPS#16
Physics-informed Neural Networks (PINN) iNote#17 iColab#17 pdf#17 pptx#17 PS#17 iPS#17
AI in Mechanical Engineering iNote#18 iColab#18 pdf#18 pptx#18 PS#18 iPS#18
Final Exam Part I Part II