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Mathematics for Machine Learning- CST 284 - KTU Minor Notes - Dr Binu V P

 Introduction

Linear Algebra in Machine Learning

Module I- Linear Algebra

1.Geometry of Linear Equations(video-Gilbert Strang)
2.Elimination with Matrices(video-Gilbert Strang)
6. Practice problems Gauss Elimination ( contact)


More Example Problems ( contact)

Module-II -Analytic  Geometry and Matrix Decomposition

*Practice Problems ( contact)

Module III -Vector Calculus

4.Practice Problems ( contact)
14.Practice Problems ( contact)

Module IV- Probability and Distributions


Module V- Optimization


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1.1 Solving system of equations using Gauss Elimination Method

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4.3 Sum Rule, Product Rule, and Bayes’ Theorem

 We think of probability theory as an extension to logical reasoning Probabilistic modeling  provides a principled foundation for designing machine learning methods. Once we have defined probability distributions corresponding to the uncertainties of the data and our problem, it turns out that there are only two fundamental rules, the sum rule and the product rule. Let $p(x,y)$ is the joint distribution of the two random variables $x, y$. The distributions $p(x)$ and $p(y)$ are the corresponding marginal distributions, and $p(y |x)$ is the conditional distribution of $y$ given $x$. Sum Rule The addition rule states the probability of two events is the sum of the probability that either will happen minus the probability that both will happen. The addition rule is: $P(A∪B)=P(A)+P(B)−P(A∩B)$ Suppose $A$ and $B$ are disjoint, their intersection is empty. Then the probability of their intersection is zero. In symbols:  $P(A∩B)=0$  The addition law then simplifies to: $P(A∪B)=P(A)+P(B)$  wh
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