KIET-LMS

Linear Algebra-I:  Matrices and their properties, rank by echelon form, Rank, nullity, Eigen Values and Eigen Vectors, Inner Product and projections.

Linear Algebra-II: Similarity transformation, Diagonalization (2x2), EVD (Eigen Value Decomposition, SVD (Singular Value Decomposition), Matrix Factorization, LU- Doolittle Method.

Probability and Distributions: Probability and Conditional Probability, Bayes’ Theorem, Discrete and Continuous Distributions, Binomial Distribution, Poisson Distribution., Normal Distribution , Expectation and variance.

Descriptive Statistics and Interference: Mean, Median, Mode, Standard Deviation, Correlation, Sampling Techniques Sampling Theory (Small and Large), Hypothesis, Null hypothesis, Alternative hypothesis, Testing a Hypothesis, Level of significance, Confidence limits, t-test.

Calculus and Optimization Basics: Partial Derivatives, Composite Function, Total Derivatives, Chain Rule and Gradient Computation, Gradient Descent Algorithm, Learning rate and convergence.