DS 303 - Introduction to Machine Learning

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
  • 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)
Pre-requisite : CS101. Plus math courses like linear algebra, probability, calculus will be helpful
Total credits : 6 credits - Lecture
Type : Theory
Duration : Full Semester