Mathematics In AI: Mathematical concepts such as statistics and probability form the foundation of Artificial Intelligence (AI) and its branches like Machine Learning and Deep Learning. Machine Learning focuses on learning from data and making predictions, while Deep Learning works on recognizing complex patterns in massive datasets, similar to how the human brain functions. Statistics help us understand large volumes of data, while probability deals with uncertainties that arise when building AI models.
Let's see how AI is built on core concepts like Statistics, Probability:
1. Probability as the Language of Uncertainty
Real-world data is often uncertain and noisy, sometimes due to errors during collection. Probability helps handle this uncertainty. For example, in email classification, probability can determine if a message has a 95 per cent chance of being spam.
2. Basis of Core Algorithms
Several core algorithms used for Machine Learning concepts and AI are built on Mathematical foundations. The algorithm "Naive Bayes Classifier" leverages Bayes' theorem, a probability concept which states that all features are independent of each other. Other algorithms using mathematical concepts include the Logistic Regression, Neural Networks , Hidden Markov Models and more.
3. Understanding the Data
AI learns from data and statistical concepts such as the mean, variance, standard deviation, correlation, and distribution help in identifying data patterns through summarization and visualization.
4. Model Evaluation
During the making of an AI model, Performance metrics like accuracy, precision, recall, F1-score, ROC curves are used to check the accuracy of the model which helps improve it further. They help determine if a model is truly learning or just memorizing.
5. Feature Engineering and Selection
Mathematical Concepts like correlation, variance, and mutual information help decide which features (inputs) matter for predictions. For example- When predicting a price of house through features like its location, size and condition. The features here represent the inputs and price is the output. Through Statistics, we can identify whether location as an input is important or not.
6. Optimization and Learning
Many Machine Learning algorithms use Gradient Descent (a Mathematical concept) to fix their losses - which when done helps the AI to make more accurate predictions.
A Machine Learning model learns to predict new values or classes through training and learning on data already available to it. The data includes the value required to be predicted.
Statistics and Probability together form the backbone of AI- the technology driving the future. A strong grasp of mathematics from school itself prepares students for advanced topics in AI and Machine Learning
