ABOUT THE COURSE:
This course will introduce students and practitioners of core engineering disciplines to the fundamentals and applications of machine learning. The course will cover the following material: introduction to data science and machine learning, examples of supervised/unsupervised/reinforcement learning, motivation for machine learning with examples derived from various core engineering disciplines; introduction to Python, scientific computing packages (NumPy, SciPy, Matplotlib), and simple ML packages (Scikit-learn, Pytorch); linear and nonlinear regression, confidence intervals and goodness of fit, loss functions, gradient descent algorithm, overfitting/underfitting, regression/classification learning; clustering algorithms, singular-value decomposition, principal component analysis, and nonlinear dimensionality reduction; decision trees and ensemble methods, boosting and bagging techniques, random forests, gradient-boosted machine learning, support vector machines, Gaussian process regression; hyperparameter tuning and cross validation; introduction to neural networks and deep learning; feed-forward, convolutional, and recurrent neural networks
INTENDED AUDIENCE: Core Engineering Disciplines
INDUSTRY SUPPORT: Several companies working in core engineering disciplines
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