Role of Mathematics in Machine Learning

 RAKSHITHA A
I SEM, BCA, NCMS

Four pillars for Machine Learning

The mathematics of machine learning theory is a field that intersects statistical, probabilistic, computer science, algorithmic aspects and finding hidden insights which can be used to build intelligent applications. 

Importance of mathematics in machine learning

• Selecting the right algorithm which includes giving consideration to accuracy, training, time, model complexity, number of parameters and number of features.
• Choosing parameter settings and validation strategies.
• Identifying under fitting and over fitting by understanding the Bias variance trade off.
• Estimating the right confidence interval and uncertainty.

Advantages :

• Easily identifies trends and patterns
• Continuous improvement
• No human intervention needed
• Wide applications
• Handling multi-dimensional and multi-variety data

Disadvantages :

• Data Acquisition
• Time and Resources
• Interpretation of results
• High error susceptibility

        Mathematics for machine learning is an essential facet that is often overlooked or approached with the wrong perspective. As a soft prerequisite, there is an expectation that one should have a fair understanding of the mathematical concepts.