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Language of Instruction
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English
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Level of Course Unit
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Doctorate's Degree
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Department / Program
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ELECTRICAL AND COMPUTER ENGINEERING
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Type of Program
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Formal Education
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Type of Course Unit
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Elective
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Course Delivery Method
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Face To Face
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Objectives of the Course
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OB 1: Interpret core optimization principles and their mathematical underpinnings essential for machine learning practice.
OB 2: Developing fundamental optimization algorithms using Python for diverse machine learning tasks.
OB 3: Compose real-world machine learning development challenges using Python-based tools and techniques.
OB 4: Applying optimization techniques to train and improve various machine learning models, using Python coding for practical implementation and evaluation.
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Course Content
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This practical course provides a hands-on, coding-intensive introduction to optimization for machine learning. It covers fundamental optimization algorithms like Gradient Descent and its variants, adaptive learning methods, and convex optimization. Taught through extensive Python coding labs and exercises, the course emphasizes practical implementation, visualization, and performance benchmarking. Students will learn to apply optimization techniques to build and improve real-world machine learning models, gaining strong practical Python skills and a solid foundation for future AI studies.
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Course Methods and Techniques
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Prerequisites and co-requisities
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None
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Course Coordinator
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None
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Name of Lecturers
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Asist Prof.Dr. KHALED A M HEJJA
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Assistants
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None
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Work Placement(s)
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No
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