Machine learning is all around by math, which in return helps in building an algorithm which can acquire from data to create a precise prediction. The estimate might be as humble as categorizing dogs or cats from a given set of images or what type of goods to endorse to a client based on previous purchases. Therefore, it is very essential to correctly recognize the math’s concepts after any vital machine learning algorithm. This means, it helps you to best choice all the true algorithms for our scheme in data science and machine learning. Machine learning is mainly built on mathematical fundamentals so as long as you can recognize why the math’s is used, you will discover it more exciting. With this, you will appreciate why we choice one machine learning algorithm above the other and how it marks the presentation of the machine learning model.
Why we need Math in machine learning developments:
There are several causes why mathematics for Machine Learning is important, and I will be distributing a few of the significant indicators below. Selecting the greatest algorithm needs taking into account precision, preparation time, model difficulty, number of parameters, and number of structures. Selecting parameter standards and authentication techniques. Accepting the Bias-Variance tradeoff agrees you to categorize under fitting and overfitting matters which usually take place during executing the program. Defining the accurate self-assurance interval and doubt.
How is AI associated with mathematics?
Artificial intelligence difficulties establish on two over-all sorts, Search Problems as well as Representation Problems. Both of these are interrelated copies and tools such as Rules, frames, Logics as well as Nets. All are much more mathematical subjects. The main resolution of Artificial intelligence is to make a suitable prototype for human accepting. And these representations can be organized with the concepts and plans from numerous divisions of Mathematics. Such as self-driving cars, their goal is to identify matters and public in videos images. Here is mathematics behind these cars in the arrangement of minimization actions as well as back-propagation. Mathematics supports AI scientists to resolve challenging shallow abstract issues using traditional techniques and procedures well-known for many years.
This book cover these topics in detail:
- LINEAR REGRESSION
LINEAR REGRESSION - THE LEAST SQUARES METHOD
LINEAR ALGEBRA SOLUTION TO LEAST SQUARES PROBLEM - LINEAR DISCRIMINANT ANALYSIS
- LOGISTIC REGRESSION
- THE MULTIVARIATE NEWTON-RAPHSON METHOD
MAXIMIZING THE LOG-LIKELIHOOD FUNCTION - ARTIFICIAL NEURAL NETWORKS
- ESTIMATING THE OUTPUT FUNCTIONS
- ERROR FUNCTION FOR REGRESSION
- ERROR FUNCTION FOR BINARY CLASSIFICATION
- ERROR FUNCTION FOR MULTI-CLASS CLASSIFICATION
- MINIMIZING THE ERROR FUNCTION USING GRADIENT DESCENT
- MAXIMAL MARGIN CLASSIFIER
- DEFINITION OF MAXIMAL MARGIN CLASSIFIER
- REFORMULATING THE OPTIMIZATION PROBLEM
- SOLVING THE CONVEX OPTIMIZATION PROBLEM
- SUPPORT VECTOR CLASSIFIER
- SUPPORT VECTOR CLASSIFIER
- POINTS ON CORRECT SIDE OF HYPERPLANE
- POINTS ON WRONG SIDE OF HYPERPLANE
- FORMULATING THE OPTIMIZATION PROBLEM
- DEFINITION OF SUPPORT VECTOR CLASSIFIER
- A CONVEX OPTIMIZATION PROBLEM
- SUPPORT VECTOR MACHINE CLASSIFIER
Zain