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Lecture notes from "Numerical Analysis for Artificial Intelligence" course I presented at UCSD, CSE department

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# Numerical Analysis for Artificial Intelligence, Jacek Cyranka

This repository contains course materials from the "Numerical Analysis for Artificial Intelligence" course I presented at UCSD, CSE department during Summer session 2018.

## course syllabus

• Week 1,2 Review of Programming in Python+NumPy+IPython notebook and �Calculus and Linear Algebra topics

• Python language basics,
• Linear Algebra in NumPy,
• Working with Jupyter notebooks,
• Example problem of solving a linear regression analytically,
• Functions,
• Vector spaces,
• Matrices,
• Matrix times vector/matrix operation,
• Matrix transpose/inverse,
• Solving systems of linear equations,
• Basic properties,
• Partial Derivatives,
• Critical points,
• Characterization of critical points as local/global minima/maxima.
• Week 3 Gradient descent and convex optimization

• Backpropagation algorithm,
• gradient checking of a backpropagation implementation,
• Avoiding problems with convergence by decreasing the learning rate,
• Accelerated gradient descent (Nesterov momentum method),
• Solving linear regression using gradient descent.
• Week 4,5 Nonconvex optimization : supervised learning of feed-forward Neural Networks

• Difference in Convex/Nonconvex optimization,
• Classical Blum/Rivest proof that training a 3-node NN is NP-Complete,
• Perceptrons
• Single hidden layered networks,
• Linear, ReLU , tangential networks,
• Mean squared error loss, cross entropy loss,
• Multiple hidden layered feed forward networks,
• Newton’s method.

Lecture notes from "Numerical Analysis for Artificial Intelligence" course I presented at UCSD, CSE department

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