Simply being a fan of science fiction will not suffice in thriving in the field of AI, MATHEMATICS will help!
A few days back I was attending a data analysis webinar and on the first day, instead of teaching us how to use the SPSS software for data analysis, they focused on probability, multivariate analysis, and calculus. My mother was also attending it, and I asked her, mom, I am here to learn data analysis, and how to use the software. Why are they teaching probability and all those mathematical concepts here?
My mother then gave me, perhaps, the best piece of advice and at the right time. She said - The very existence of AI depends on the mathematics of computation.
Now you tell me, The data that you collect contains noise, null values, outliers, and other anomalies. You, first, have to clean the data and transform it into meaningful observations to develop the foundation for further analysis of data. Right. Libraries like Numpy, Pandas that you use for such tasks also rely on predefined mathematical functions.
Only when you have understood and analyzed the data, will you be able to determine which model is best for you. The model in itself is created using strategies from several disciplines of mathematics. Simply being a fan of science fiction will not suffice in thriving in the field of AI, mathematics will help.
Artificial intelligence is heavily dependent on statistics, which requires linear algebra and calculus to make any sense at all.
1) Statistics works with vast volumes of data and is a key aspect in an organization's growth.
2) Since machine learning revolves around probable yet not mandatory situations, probability plays a crucial role in approximating the analysis.
3) Calculus deals with optimizing the performance of machine learning models and is used in the creation of artificial neurons. It is used in back propagation algorithms to train deep Neural Networks. The optimizers like, gradient descent, Root Mean Square Propagation, Adagrad help in reaching the Global Minima with the lowest loss and most accurate output. It is impossible to compute probabilities on data and get plausible outcomes without having an understanding of calculus
4) Linear algebra, which constitutes Tensors, matrices, sets and sequences, helps us comprehend the background theory of Machine Learning.
Apart from that, the overall concept of thinking machines and mimicking human behaviors is done with the help of mathematical concepts. Checking whether the model is overfitting or underfitting is also determined using mathematical functions. We use several vector techniques to address problems such as regression, clustering, and speech recognition.
Lately, computer scientists have been able to address the problem of transportation leaks by examining data currently in the water network and finding patterns as quickly as possible using complex mathematical algorithms and statistical models!
In all, the mathematical functions assist us in visualizing the content of the dataset and in gaining a better grasp of the problem we are attempting to solve using a machine learning algorithm.
Just keep in mind that nothing about AI is magical; every thought is linked to Mathematics, which is what creates magic behind all the inventions.
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