Mathematics for Machine Learning CS-632

    Pre-requisites

    There are no official pre-requisites for this course.

    Venue: CSE Department Conference Hall (2nd floor)

    Time Slot:

    Contents

  1. Probability Theory, Linear Algebra, Convex Optimization
  2. Introduction: Statistical Decision Theory - Regression, Classification, Bias Variance
  3. Linear Regression, Multivariate Regression, Subset Selection, Shrinkage Methods, Principal Component Regression, Partial Least squares
  4. Linear Classification, Logistic Regression, Linear Discriminant Analysis, Perceptron, Support Vector Machines
  5. Neural Networks - Introduction, Perceptron Learning, Backpropagation, Initialization, Training & Validation, Parameter Estimation - MLE, MAP, Bayesian Estimation
  6. Decision Trees, Regression Trees, Stopping Criterion & Pruning loss functions, Categorical Attributes, Multiway Splits, Missing Values, Decision Trees - Instability Evaluation Measures
  7. Gradient Boosting, Random Forests, Multi-class Classification, Naive Bayes, Bayesian Networks, Undirected Graphical Models, HMM, Variable Elimination, Belief Propagation
  8. Gaussian Mixture Models, Expectation Maximization, Learning Theory, Introduction to Reinforcement Learning
  9. Partitional Clustering, Hierarchical Clustering, Birch Algorithm, CURE Algorithm, Density-based Clustering

    Reference Books/Text Books

  1. The Elements of Statistical Learning, by Trevor Hastie, Robert Tibshirani, Jerome H. Friedman [ Online Book ]
  2. Pattern Recognition and Machine Learning, by Christopher Bishop
  3. Christopher M. Bishop, Neural Networks for Pattern Recognition.

    Other Important Material

  1. Linear Algebra [Online MIT Course ]
  2. Machine Learning [Andrew NG's Stanford course ] [Andrew NG's Coursera course ]
  3. Essence of Linear Algebra [ 3blue1brown ]