This course covers the application of mathematics in machine learning and deep learning. It introduces students to fundamental mathematical concepts such as linear algebra, calculus, probability theory, and optimization. The course then explores various machine learning algorithms and deep learning models, including supervised and unsupervised learning, neural networks, and deep learning architectures. Students gain hands-on experience through programming assignments and projects using popular libraries like TensorFlow or PyTorch. The course emphasizes the mathematical foundations of these techniques, enabling students to understand and analyze their performance. By the end of the course, students will be proficient in applying mathematics to solve real-world problems in machine learning and deep learning.