Optimization Technique BCS405C

Module 1 Overview

VECTOR CALCULUS

Functions of several variables, Differentiation and partial differentials, gradients of vector-valued functions, gradients of matrices, useful identities for computing gradients, linearization and multivariate Taylor series.

(8 hours)

(RBT Levels: L1, L2 and L3)

Module 2 Overview

APPLICATIONS OF VECTOR CALCULUS

Backpropagation and automatic differentiation, gradients in a deep network, The Gradient of Quadratic Cost, Descending the Gradient of Cost, The Gradient of Mean Squared Error.

(8 hours)

(RBT Levels: L1, L2 and L3)

Module 3 Overview

Convex Optimization-1

Local and global optima, convex sets and functions separating hyperplanes, application of Hessian matrix in optimization, Optimization using gradient descent, Sequential search 3-point search and Fibonacci search.

(8 hours)

(RBT Levels: L1, L2 and L3)

Module 4 Overview

Convex Optimization-2

Unconstrained optimization -Method of steepest ascent/descent, NR method, Gradient descent, Mini batch gradient descent, Stochastic gradient descent.

(8 hours)

(RBT Levels: L1, L2 and L3)

Module 5 Overview

Advanced Optimization

Momentum-based gradient descent methods: Adagrad, RMSprop and Adam.

Non-Convex Optimization: Convergence to Critical Points, Saddle-Point methods.

(8 hours)

(RBT Levels: L1, L2 and L3)

Optimization Technique BCS405C is a VTU course covered through module-wise syllabus, notes, and PYQ-driven exam practice available on this page.

Credits for BCS405C: 03.

Yes. You can access organized notes, PDFs, and PYQ material from the file explorer/resources section on this page.

Start with module summaries, solve recent PYQs unit-wise, and finish with complete paper practice under time constraints for SEE readiness.

Yes, this page is maintained with current scheme-oriented materials and practical exam-focused resource curation.

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