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Mathematical Foundations for Machine Learning

NPTEL via Swayam

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ABOUT THE COURSE:This course will provide a holistic approach to the mathematical foundations for Machine Learning. The course is focussed on developing mathematical ideas, necessary for machine learning applications, through intuitions and visualizations.The course primarily focuses on three important mathematical domains, namelyLinear AlgebraProbabilty and Statistics andMultivariable Calculus,on which the ML and data science ideas are built.INTENDED AUDIENCE: BE/BTech/ME/MTech//BSc/MSc(Maths)/MCAPREREQUISITES: Basic Mathematics at school and undergraduate levelINDUSTRY SUPPORT: Amazon, Flipkart, Robert Bosch, Qualcomm, Nvidia and Companies that are into Computer vision, Data Science, Robotics and Control

Syllabus

Week 1: Vectors, Vector Spaces and Subspaces
Week 2:Linear Transformations, eigenvalues and eigenvectors
Week 3:Orthogonality, Projection and Real symmetric matrices
Week 4:Singular value decomposition, Principal Component Analysis, Support Vector Machines and Applications
Week 5:Probability Foundations - From Events to Bayes’ Theorem
Week 6:Random Variables, Moments of Random Variables
Week 7:Jointly Distributed Random Variables, Conditioning of Random variables
Week 8:Limit Theorems, Sample Geometry, Covariance Matrices and Properties
Week 9:Taylor’s series expansion, Chain rule
Week 10:Gradient, Hessian, Gradient Descent Algorithms
Week 11:Neural Nets, Perceptron, Back Propagation Algorithm
Week 12:Algorithms for ML - Classification, Clustering and Regression