Overview of Supervised Learning, Basis Expansions and Regularization, Kernel smoothing, Model assessment and Selection, Model Inference, Additive Models, Trees & Related Methods, Boosting and Additive Trees, Support Vector Machines and Flexibilities, Prototype methods and Nearest Neighbors, Unsupervised Learning, Ensemble Learning, Undirected graphical Models, High dimensional Problems.
At the end of the course the students will be able to;
| Course Outcome | Bloom’s Taxonomy Level | |
| CO 1 | Overview of Supervised Learning, Basis Expansions and Regularization | L2 |
| CO 2 | Unsupervised Learning, Ensemble Learning | L2 |
| CO 3 | Model assessment and Selection, Model Inference, Additive Models | L3 |
| CO 4 | Support Vector Machines and Flexibilities | L3 |
| CO 5 | Prototype methods and Nearest Neighbors, Undirected graphical Models, High dimensional problems | L4 |
| CO 6 | Implementation of Additive models, SVM and its variants. | L5 |
‘Statistical Learning Theory’ is an elective course offered for the M. Tech. in Computer Science and Engineering program at School of Engineering, Amrita Vishwa Vidyapeetham.
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