Description:
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Course Description: This course provides a foundation for understanding machine learning and its applications by taking a learn-by-doing approach. Students will learn about machine learning by training a regression model to perform data analysis.
Prerequisites: None
Learning Outcome:
– Review of linear algebra: To review basic concepts in linear algebra including norm, inner product, linear combination, basis functions, matrix-vector multiplication, eigen decomposition
– Principles of regression: To understand the basic principles of regression, including loss function, linear models, parameter update
– Solving a regression problem: To derive the linear least squares solutions and understand the properties under-determined and over-determined linear systems
– Regularization techniques: To apply ridge regression techniques and LASSO regression techniques
– Optimization: To review constrained and unconstrained minimization, Lagrange multiplier, convexity, gradient descent, and stochastic gradient descent
| Module |
Topic & Readings |
| Module 1 |
Why do we need linear algebra in machine learning?
Inner products and norms
Matrix Calculus Eigenvalues and eigenvectors
Principal Component Analysis Eigenface Problem |
| Module 2 |
What is regression?
How does regression work?
Solving Linear Regression and Matrix-vector form of linear regression |
| Module 3 |
Overdetermined and undetermined lease squares
Robust Linear Regression
Solving the Robust Regression Problem |
| Module 4 |
Ridge Regression and Implementation
LASSO Regularization
LASSO for Overfitting |
| Module 5 |
Convexity Lagrange and Multiplier Solving
Simple Constrained Optimization
Gradient Descent Convergence and Momentum Acceleration |
Faculty: Stanley Chan