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.

Same as Reference

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):

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.