ABOUT THE COURSE: Optimisation is the workhorse of machine learning. Knowing optimisation is a key prerequisite in understanding theory and practise of machine learning. In this course, we will discuss the foundations required for solving optimization problems in the context of machine learning through various case-studies/running-examples. We will start with covering the basics of linear algebra and calculus required for learning optimization theory. We will learn both the theory and implement optimization algorithms like stochastic gradient descent and its various variants to solve machine learning problems of classification, clustering etc using standard problem formulations which are convex (SVM etc) and non-convex (Neural Networks and Deep Neural Networks) etc. INTENDED AUDIENCE: UG/PGPREREQUISITES: Linear Algebra, Calculus, Basic ProgrammingINDUSTRY SUPPORT: Google, Microsoft, Facebook, Amazon, Flipkart and all companies connected to Data Science, Signal Processing and AI/ML
Optimisation for Machine Learning: Theory and Implementation (Hindi)
Indraprastha Institute of Information Technology Delhi and NPTEL via Swayam
Overview
Syllabus
1. Foundations of Data Science, Avrim Blum and Ravi Kannan, Hindustan Book Agency/Cambridge University Press
2. Linear Algebra and Learning from Data, Gilbert Strang
3. Convex Optimisation by Stephen Boyd
4. Optimisation for Machine Learning by Suvrit Sra, MIT Press.
2. Linear Algebra and Learning from Data, Gilbert Strang
3. Convex Optimisation by Stephen Boyd
4. Optimisation for Machine Learning by Suvrit Sra, MIT Press.
Taught by
Prof. Pravesh Biyani
