Review of machine learning Concepts, Design of ML system – data cleaning, feature engineering, model selection, model building & fine tuning, and model deployment. Bias, variance, learning curves, and error analysis. Recommendation Systems – Model for Recommendation Systems, Utility Matrix, Content-Based Recommendations, Discovering Features of Documents, Collaborative Filtering. Usage of UV and NMF decomposition in Recommendation systems
Advertising on the Web: Issues in Online Advertising, Online and offline algorithms, The matching Problem, The AdWords Problem, The Balance Algorithm, A Lower Bound on Competitive Ratio for Balance. Customer segmentation – Subspace Clustering, Types of Subspace clustering, Top down and bottom-up approach: PROCLUS and, CLIQUE and their applications in Indexing in databases. Application of dimensionality reduction-SVD for Latent Semantic Indexing, CUR for approximate query processing from databases, PCA, for Image Processing – compression, identification and Visualization.
Sparse models, State space models, Markov Decision Process, Bellman equations, Value iteration and Policy iteration, Linear Quadratic Regulation (LQR), Non-linear dynamics to LQR, Linear Quadratic Gaussian (LQG), Independent component Analysis (ICA) for speech processing