Syllabus
Kernel Methods: reproducing kernel Hilbert space concepts, kernel algorithms, multiple kernels, graph kernels; multitasking, deep learning architectures; spectral clustering ;model based clustering, independent component analysis; sequential data: Hidden Markhov models; factor analysis; graphical models; reinforcement learning; Gaussian processes; motiff discovery; graph based semi supervised learning; natural language processing algorithms.
Text Books
Same as Reference
References
1. Pattern Recognition and Machine Learning, Bishop, C. M., Springer, 2006.
2. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Hastie, T., Tibshirani, R., and Friedman, J., Springer, 2002.
3. An Introduction to Support Vector Machines and other kernel- based methods, Cristianini, N. and Shawe-Taylor, J., Cambridge Univ. Press, 2000.
4. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, Scholkopf, B. and Smola, A. J., The MIT Press, 2001.
5. Reinforcement Learning: An Introduction, Sutton R. S. and Barto, A. G., The MIT Press, 2017.
6. Deep Learning, Goodfellow, I., Bengio, Y., and Courville, A., The MIT Press, 2016.
7. Probabilistic Graphical Models: Principles and Techniques, Koller D. and Friedman, N., The MIT Press, 2009.
Course Outcomes (COs):
CO1: Provide students with an in-depth knowledge of advanced machine learning concepts.
CO2: Introduce the mathematical and statistical concepts that form the basis of advanced machine learning models.
CO3: Foster critical thinking and problem-solving skills by challenging students to analyze and critique the strengths and limitations of advanced machine learning models in various applications and contexts.