This course integrates mathematical foundations with engineering applications to provide an understanding of machine learning. Students will apply fundamental mathematical conceptssuch as probability, linear algebra, and optimizationto formulate and solve machine learning problems. Through hands-on MATLAB exercises, students will preprocess, explore, and analyze data, implement classification and regression models, and evaluate their performance using mathematical metrics. The course also explores the theoretical and practical aspects of deep learning, including backpropagation, convolutional neural networks (CNNs), and transfer learning, while emphasizing optimization techniques for improved model accuracy. By interweaving mathematical theory with engineering applications, this course ensures a well-rounded approach to machine learning, preparing students to tackle real-world challenges with both analytical and computational skills.
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