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Lecturer(s)
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Moosaei Hossein, Dr. Ph.D.
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Course content
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1. Introduction to Optimization (Convex sets, convex functions, and unconstrained and constrained optimization problems) 2. Optimality Conditions for Unconstrained and Constrained Optimization 3. Duality Theory 4. Optimization Techniques in MATLAB 5. Introduction to Data Representation and Mining 6. Support Vector Machines 7. Proximal Support Vector Machines 8. Twin Support Vector Machines 9. Clustering by k-means 10. Validation Methods 11. Machine Learning with MATLAB 12. Massive Data Sets and Future Challenges
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Learning activities and teaching methods
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unspecified
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Learning outcomes
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This course teaches an overview of optimization methods, for applications in machine learning and data science by using MATLAB. Indeed, Optimization for Machine Learning with MATLAB provides an insight into the theory background and applications of supervised and unsupervised learning algorithms in MATLAB. MATLAB is one of the best tools for assignments and course projects, but if you have other preferences, you can use different suitable environments, such as Python.
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Prerequisites
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Programming language (MATLAB or Python)
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Assessment methods and criteria
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unspecified
project assignment
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Recommended literature
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