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

Faculty: Stanley Chan

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Course Description: Students will work through real-world problems and examples to understand the mathematical background for AI. By breaking concepts down and putting them in context, this course makes the math behind AI accessible for a wider audience. 

Prerequisites:
Basic probability
Linear algebra
Ordinary differential equations (knowledge of ordinary differential equations will be valuable but is not required in order to be successful)

Learning Outcome: 
– Analyze equations involving matrices by applying algebraic concepts such as rank, nullspace, linear independence, and eigenvalues 
– Define properties of linear systems, including controllability, observability, and stability, and apply them to design state estimators and feedback controllers 
– Define probability distributions and moments of random variables, and characterize the long-term behavior of stochastic processes 
– Specify the fundamental optimality conditions for optimization problems, and implement basic algorithms to find the optimizers 

Faculty: Philip E. Paré and Shreyas Sundaram  

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Course Description: This course provides in-depth conceptual explanation of supervised and unsupervised machine learning algorithms and how to implement them to address real-world problems.  

Prerequisites: None

Learning Outcome: 
– Assess and understand the core principles, applications, and limitations of machine learning, distinguishing its role from traditional programming
– Analyze a variety of supervised and unsupervised machine learning algorithms using prominent frameworks
– Integrate machine learning knowledge to address real-world challenges by choosing appropriate algorithms and techniques

Faculty: Jin Kocsis

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