To understand the key mathematical techniques in machine intelligence and learn their applicability to practical manufacturing problems. Topics covered in the course include: Best Approximation, Linear & Logistic Regression, Matrix Decomposition, Diagonalization & Similarity, Fourier Transformation, Laplace Transformation, Dimensionality Reduction with PCA and SVD, Classification with Support Vector Machine, Gradient Descent Optimization, Taylor Approximation, and Neural Networks

Linear Algebra, Calculus, Programming Skills

Midterm project 15%
Final project 15%
Midterm exam 35%
Final exam 35%

• Howard Anton and Chris Rorres, Elementary Linear Algebra, 10th ed., John Wiley & Sons, 2011
• Alan Oppenheim and Alan Willsky, Signals & Systems, 2nd ed., Prentice Hall: New Jersey, 1997
• Douglas C. Montgomery and George C. Runger, Applied statistics and Probability for Engineers, 5th ed., John Wiley & Sons, 2011

1 Approximation (general vector space, projection, orthogonality)
2 Approximation (linear regression)
3 Eigen Decomposition (spectral decomposition)
4 Principal Component Analysis (diagonalization & similarity)
5 Principal Component Analysis (dimension reduction)
6 Singular Value Decomposition
7 Fourier Transformation
8 Midterm
9 Laplace Transformation
10 Constrained Optimization (constraints and Lagrange multipliers)
11 Support Vector Machine
12 Gradient Descent Optimization (partial derivatives, chain rule, Hessians and second-order derivatives)
13 Logistic Regression, Neural Networks and Backpropagation
14 Activation Functions, Loss Functions
15 Talyor Approximation & Applications
16 Final Exam

Flipped Learning, Lectures, Programming projects