Course content

• Physical storage and indexing structures, Query processing algorithms, Query optimization, Transaction processing and serializability, Concurrency Control, Recovery
• Big data management: transaction and query processing on parallel and distributed databases including issues of availability, replication, consistency, concurrency control, and recovery. Emerging database trends
• Introduction to ML
• Idea of supervised, unsupervised, semi-supervised, reinforcement learning
• Linear regression
• Idea of model complexity, generalization, bias-variance trade-off, regularization
• Cross validation, VC dimension
• Supervised classification algorithms: K nearest neighbor, LDA, Decision Tree, SVM and kernel methods, Neural Network, Naive Bayes', Gaussian discriminant analysis, Ensemble methods etc.
• More on probabilistic learning models: Parameter estimation using MLE, MAP, GMM, EM algorithm
• Unsupervised learning: Clustering and kernel density estimation, K-means, DBSCAN, Parzen window technique etc.
• Dimensionality reduction using PCA and kernel PCA
• Intro to reinforcement learning
• Intro to deep learning and convolutional networks, recurrent networks
• Some advanced learning topics if time permits

References

• Pattern Recognition and MachineLearning, by Christopher Bishop,Springer 2011
• The Elements of Statistical Learning:Data Mining, Inference, and Prediction,Second Edition, by Trevor Hastie andRobert Tibshirani (Springer Series inStatistics) 2016
• Supplementary material available online,e.g. Dive into Deep Learning by AstonZhang, Zack C. Lipton, Mu Li andAlexander Smola, 2020 (https://d2l.ai)