In this course, you will learn about Bayes Theorem and decision making using Bayes Theorem, designing and customizing the Bayesian theorem to work with more than 2 features and cost estimation for errors.
Upon successful completion of the course, the learner will be able to
Master the various concepts in Statistical Decision Making.
Learn how Statistical Decision Making plays an important role in prediction and decision making in AI and ML algorithms.
In this topic, you will learn about how conditional probability is used to derive Bayes Theorem and decision making using Bayes Theorem.
In this topic, you will learn about designing and customizing the Bayesian theorem to work with more than 2 features.
In this topic, you will learn about how to create decision region classification by deriving Decision Boundaries and multidimensional Decision Boundaries.
In this topic, you will learn about how to calculate the cost of error and how to do an estimation of errors.
In this topic, you will learn about the application of Statistical Decision Making in the field of AI and ML.
Learning through E-Box is completely Hands-on and practice based thereby helping you understand the intricacies of Live Project Scenarios
E-Box’s programs and Courses are designed by Professionals in the Industry & Academicians, assuring a quality learning experience
With 100’s Mentors online ready to get your Doubts clarified and to Hand Hold you, you will always have someone to sort things our for you
All our Mentors are Top Professionals from their respective Industries. In E-Box you Learn and get mentored by professionals
Auto Evaluation is the Core of E-Box. Your solutions and answers are evaluated instantaneously by E-Box, thereby saving learners a lot of time
With Auto - Evaluation being the core of the Platform, E-Box uses AI & ML to assist Learners during their course of Study
E-Box’s AI algorithms provide you with Exercises that are Personalised and Adaptive based on your Interest and Calibre
Data becomes the core for evolving a personalised learning. E-Box uses these metrics for an Auto Corrective Learning Process