Syllabus
Theory of reproducing kernel Hilbert space, support vector machines, kernel ridge regession, kernel feature extraction, kernel online learning, Bayesian kernel methods, graph kernels, kernels for text, kernels for structured data.
Text Books
Same as Reference
References
- Bishop, C. M., Pattern Recognition and Machine Learning, Springer (2006).
- Cristianini, N. and Shawe-Taylor, J., An Introduction to Support Vector Machines and other kernel-based methods, Cambridge Univ. Press (2000).
- Scholkopf, B. and Smola, A. J., Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, The MIT Press (2001).
- Shawe-Taylor, J. and Cristianini, N., Kernel Methods for Pattern Analysis, Cambridge Univ. Press (2004)
Course Outcomes (COs):
CO1: Understanding the theoretical foundations: Students will develop a solid understanding of the theoretical foundations of kernel methods.
CO2: Implementing kernel-based algorithms: Students will learn how to implement kernel-based algorithms.
CO3: Applying kernel methods in real-world problems: The course will provide hands-on experience with applying kernel methods to real-world datasets and problems, preparing students to use them in their own research or projects.