Gain a deep understanding of key concepts, algorithms, and techniques that power modern AI systems.     Through hands-on projects and real-world case studies, you will learn to preprocess data, build predictive models,     evaluate their performance, and interpret results. Develop the skills to make informed decisions, solve complex problems,     and harness the potential of AI and statistical modeling across various domains. Whether you are a beginner or seeking to     enhance your expertise, this course equips you with the tools to thrive in the data-driven landscape.

Learning Outcomes
At the end of the course, you should be able to:

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• - Review of linear algebra: To review basic concepts in linear algebra including norm, inner product,         linear combination, basis functions, matrix-vector multiplication, and eigen decomposition.
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• - Principles of regression: To understand the basic principles of regression, including loss function,         linear models, and parameter updates.
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• - Solving a regression problem: To derive the linear least squares solutions and understand the properties         of under-determined and over-determined linear systems.
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• - Regularization techniques: To apply ridge regression techniques and LASSO regression techniques.
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• - Optimization: To review constrained and unconstrained minimization, Lagrange multiplier, convexity, gradient descent, and stochastic gradient descent.

*This course is a joint collaboration between UCSI and Purdue University. Upon completion, participants will receive a certificate co-issued by both UCSI and Purdue University.