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 semisupervised learning; natural language processing algorithms.
References
1. Bishop, C. M., Pattern Recognition and Machine Learning, Springer (2006).
2. Hastie, T., Tibshirani, R., and Friedman, J., The Elements of Statistical Learning: DataMining, Inference, and Prediction, Springer (2002).
3. Cristianini, N. and Shawe-Taylor, J., An Introduction to Support Vector Machines and otherkernel- based methods, Cambridge Univ. Press (2000).
4. Scholkopf, B. and Smola, A.J., Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, The MIT Press (2001).
5. Sutton R. S. and Barto, A. G., Reinforcement Learning: An Introduction, The MIT Press (2017).
6. Goodfellow, I., Bengio, Y., and Courville, A., Deep Learning, The MIT Press (2016).
7. Koller D. and Friedman, N., Probabilistic Graphical Models: Principles and Techniques, The MIT Press (2009).
Course Outcomes (COs):
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.