BTECH IN ELECTRONICS AND COMMUNICATION ENGINEERINGcoretheory
ENGINEERING MATHEMATICS - IV
MAT 2227
Syllabus
- 01Construction of a Probability Space
- 02Discrete and Continuous Probabilities
- 03Sum Rule, Product Rule, and Bayes' Theorem
- 04Summary Statistics and Independence
- 05Distributions: Binomial, Poisson, uniform, normal, Chi-square and exponential distributions
- 06Two and higher dimensional random variables, covariance, correlation coefficient
- 07Moment generating function, functions of one dimensional and two dimensional random variables
- 08Static probabilities: review and prerequisites generating functions, difference equations
- 09Dynamic probability: definition and description with examples
- 10Markov chains, transition probabilities
- 11Differentiation of Univariate Functions
- 12Partial Differentiation and Gradients
- 13Gradients of Vector-Valued Functions
- 14Gradients of Matrices
- 15Useful Identities for Computing Gradients
- 16Backpropagation and Automatic Differentiation
- 17Higher-Order Derivatives
- 18Linearization and Multivariate Taylor Series
- 19Basic solution
- 20Convex sets and function
- 21Simplex Method
- 22Optimization Using Gradient Descent
- 23Constrained Optimization and Lagrange Multipliers
References
- Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong, “Mathematics for Machine Learning”, Cambridge University Press, 2020
- P L Meyer, “Introductory Probability and Statistical Applications”, Addison Wiley, 2000
- Medhi. J. “Stochastic Processes”, Wiley Eastern, 2022
Credits Structure
2Lecture
1Tutorial
0Practical
3Total