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AL-503 (C) · Optimization Techniques in Machine Leaning/Official Syllabus

Optimization Techniques in Machine Leaning (AL-503 (C)) - Official Syllabus

1
Unit 1

Introduction

  • What is optimization
  • Formulation of LPP
  • Solution of LPP: Simplex method
  • Basic Calculus for optimization: Limits and multivariate functions
  • Derivatives and linear approximations: Singlevariate functions and multivariate functions
2
Unit 2

Machine Learning Strategy

  • ML readiness
  • Risk mitigation
  • Experimental mindset
  • Build/buy/partner
  • setting up a team
  • Understanding and communicating change
3
Unit 3

Responsible Machine Learning

  • AI for good and all
  • Positive feedback loops and negative feedback loops
  • Metric design and observing behaviours
  • Secondary effects of optimization
  • Regulatory concerns
4
Unit 4

Machine Learning in production and planning

  • Integrating info systems
  • users break things
  • time and space complexity in production
  • when to retain the model?
  • Logging ML model versioning
  • Knowledge transfer
  • Reporting performance to stakeholders
5
Unit 5

Care and feeding of your machine learning model

  • MLPL Recap
  • Post deployment challenges
  • QUAM monitoring and logging
  • QUAM Testing
  • QUAM maintenance
  • QUAM updating
  • Separating Datastack from Production
  • Dashboard Essentials and Metrics monitoring

Reference Books

  • Introduction to Machine Learning — Jeeva Jose — Khanna Book Publishing — 2020
  • Machine Learning — Rajiv Chopra — Khanna Book Publishing — 2021
  • Optimization for Machine Learning — Suvrit Sra, Sebastian Nowozin, Stephen J. Wright — MIT Press — 2011
  • Optimization in Machine Learning and Applications — Suresh Chandra Satapathy, Anand J. Kulkarni — Springer — 2019
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