Basics of Numerical Methods for Machine Learning & Engg.

Numerical methods play a critical role in machine learning, deep learning, artificial intelligence, and data science. These methods are essential for solving complex mathematical problems that are common in these fields.

One of the most important uses of numerical methods in these areas is in the optimization of machine learning models. Optimization is the process of finding the set of model parameters that minimize a given objective function. This process involves complex mathematical calculations that often require numerical methods .

Here, the course is thoughtfully structured and organised. The topics covered are-

The Calculus of Finite Differences

• The Forward Differences

• Forward Difference Table

• The backward Differences

• Properties of Difference Operator

Interpolation with equal Intervals

• Assumptions for methods of Interpolation

• Newton Gregory Method/Formula

• Newton Gregory Formula for backward Interpolation

Interpolation with unequal Intervals

• Lagrange's Interpolation Formula

• Divided Difference Formula

Numerical Differentiation

Numerical Integration

• General Quadrature Formula

• Trapezoidal Rule

• Simpson's One Third (1/3) Rule

• Simpson's Three Eighths(3/8)Rule

• Weddle's Rule

Numerical Solution of Algebraic and Transcendental Equation

• Properties of Algebraic Equations

• Synthetic Division

• Derivative of a Polynomial with synthetic division

• Methods of finding out roots of equation : Graphical Method

• Bisection Method

• Regula Falsi Method/False Position Method

• Iteration Method

• Newton Raphson Method

Numerical methods are also used in the analysis of large datasets. Data scientists often encounter datasets that are too large to be processed using traditional methods. In these cases, numerical methods such as randomized linear algebra and Monte Carlo simulations can be used to efficiently process the data.

Here , in this course you'll receive support through a Q&A section, and the course is continually updated based on student feedback, with plans to add new topics in the future.

So why wait?

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