Artificial intelligence and Machine learning (ML) are ruling the digital world today. In the next 10 years, it will reach human levels.
There are probabilities that by 2045, we will have multiplied the intelligence, the human biological machine intelligence of our civilization a billion-fold.
These amazing technologies can completely change how a company works. With the increasing adoption and commercial applications of AI and ML, many IT professionals choose AI and ML as their career paths. However, given the competition to enter the field, it may not be easy to set yourself apart in a pool of candidates. But the question is, what if you are a mathematics student looking forward to a career in AI?
Undoubtedly, from virtual reality (VR) to functional gadgets, AI has become part and parcel of our lives in a way nobody has ever seen or waited for. Artificial intelligence tools and chatbots are about to make a breakthrough in the rapidly changing technological field.
However, if we look at it, it is no magic; AI is mainly about mathematics.
You need a solid foundation in mathematics to adequately understand AI/ML algorithms and apply them correctly to data. It is the mathematical concepts that help machines to think and mimic human behavior in the best possible way. Artificial intelligence and mathematics are the two arms of a single tree.
Artificial intelligence problems constitute two general categories; Search Problems and Representation Problems. Interconnected models and tools like Rules, frames, Logic, and Nets follow them. They’re all very mathematical things.
The main objective of artificial intelligence is to build an acceptable model for human understanding. In addition, such models can be prepared with various mathematics branches’ insights and strategies.
Let us dive deeper and know what maths is required to become an AI expert:
Behind all the important progress in math, the concepts of linear algebra, computation, game theory, statistics, probability, advanced logistic regressions, and gradient descent are all major building blocks of data science.
By studying maths, you can develop a great understanding of logical reasoning and learn to focus on details. It actually enhances your abilities to think under pressure and increases your mental endurance. The three major mathematical branches that make up a successful career in AI are linear algebra, calculus, and probability. This is more about the structure and the elaboration of principles that remain true even if you modify the components.
An understanding of algebra may be fundamental to math in general. Besides mathematical operations such as addition, subtraction, multiplication, and division, you will need to know the following: Exponents, radicals, factorial, scientific summations, and notations.
Rightly said by Skyler Speakman, “Linear Algebra is the mathematics of the 21st century.” It is something that AI experts swear by, and it is essential if you want to master this field.
Linear Algebra helps in generating new ideas. They can abstract data and models with the concepts of vectors, scalars, tensors, matrices, sets and sequences, Game Theory, Topology, Graph theory, functions, linear transformations, eigenvalues, and eigenvectors.
Linear algebra is the main tool for mathematical calculation in artificial intelligence and many other areas of science and engineering.
Here you need to understand four main mathematical objects and their properties: Scalar — one digit (may be real or natural), Vectors — a list of numbers in sequence, Matrices — a set of 2D numbers in which two indicia identify each number, and tensors — an N-D (N>2) matrix of numbers placed on a regular grid with N axes.
The calculation includes changes to parameters, functions, errors, and approximations. Good knowledge of computing is imperative in artificial intelligence.
The most important concepts for the calculation are:
Derivatives – rules (add, product, string rule, etc.), hyperbolic derivatives (tan, cos, etc.), and partial derivatives; Vector/Matrix Computation — Different Derived Operators (Gradient, Jacobian, Hessian, and Laplacian), Gradient algorithms include local/global maxima and minima, saddle points, convex functions, batches, minilots, stochastic gradient descent, and performance comparison.
Statistics & Probability
You might have enjoyed the most statistics and probability while studying mathematics as a kid. This topic will likely take up much of your time, but the concepts are not that difficult. It comprises the following elements:
Basic statistics — Average, median, mode, variance, covariance, etc.
Basic probability rules – events (dependent and independent), sample spaces, conditional probability; Random variables — continuous and discreet, expectations, variance, distributions (joint and conditional), Bayes’ Theorem — calculates the validity of beliefs.
Bayesian software assists machines in recognizing patterns and making decisions; Maximum Likelihood Estimation (ML) — parameter estimation, Knowledge of fundamental probability concepts (shared probability and independence of events), and Common Distributions — poisson, binomial, bernoulli, gaussian, exponential.
Information Theory Concepts
The Information Theory Concept is an amalgamation of statistics and calculus. It has made significant contributions to AI and Deep Learning and is yet unknown to many. The information theory concepts consist of the following:
Entropy — also referred to as Shannon Entropy. Used for the measurement of uncertainty in an experiment.
Cross-entropy — compares two probability distributions and shows us how similar they are.
Kullback Leibler Divergence — another measure of the similarity of two likelihood distributions.
Viterbi Algorithm — widely used in natural language processing (NLP) and speaking.
Encoder-Decoder — used in Machine Translation RNNs and other models.
Needless to say, math is fun. The deeper you go into math, be sure to understand the beauty of a certain math concept and how it affects something. You will soon share the unbridled passion that many mathematicians and AI Scientists have!
If you’re a math enthusiast willing to make a career in AI, you can probably do your best.