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Subject Syllabus

AL503 (C) - Optimization Techniques in Machine Learning

AIML · Semester 5

Course Modules

Learn topic-wise with Hyper AI and revise using mapped PYQs.

What is optimization Formulation of a linear programming problem (LPP)Solution of an LPP: simplex method Basic calculus for optimization: limits and multivariate functions Derivatives and linear approximations of single-variate and multivariate functions

ML readiness Risk mitigation Experimental mindset Build, buy, or partner Setting up a team Understanding and communicating change

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

Integrating information 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

MLPL recap Post-deployment challenges QUAM monitoring and logging QUAM testing QUAM maintenance QUAM updating Separating the data stack from production Dashboard essentials and metrics monitoring