Mathematics in AI: Mathematical concepts such as statistics and probability form the basis of artificial intelligence (AI) and its fields such as machine learning and deep learning. While machine learning focuses on learning from data and making predictions, deep learning works on recognizing complex patterns in large datasets, similar to how the human brain works. While statistics helps us make sense of large amounts of data, probability deals with the uncertainty that comes with building AI models.
Let’s take a look at how AI is built on core concepts like statistics and probability.
1. Probability as a language of uncertainty
Real-world data is often uncertain and noisy due to errors during collection. Probability helps us deal with this uncertainty. For example, email classification can probabilistically determine whether a message has a 95% chance of being spam.
2. Fundamentals of core algorithms
Machine learning concepts and some of the core algorithms used in AI are built on mathematical foundations. The algorithm “Naive Bayes Classifier” leverages Bayes’ theorem, a probability concept that states that all features are independent of each other. Other algorithms that use mathematical concepts include logistic regression, neural networks, and hidden Markov models.
3. Understand your data
AI learns from data, and statistical concepts such as mean, variance, standard deviation, correlation, and distribution can help identify data patterns through summarization and visualization.
4. Model evaluation
While creating an AI model, you can use performance metrics such as precision, precision, recall, F1 score, and ROC curve to check the model’s accuracy and further improve it. These help determine whether the model is really learning or just remembering.
5. Feature engineering and selection
Mathematical concepts such as correlation, variance, and mutual information help determine which features (inputs) are important for prediction. For example, predicting the price of a home based on characteristics such as location, size, and condition. Function here represents input and price represents output. Through statistics, we can identify whether position as an input is important or not.
6. Optimization and learning
Many machine learning algorithms use gradient descent (a mathematical concept) to correct for loss. This allows the AI to make more accurate predictions.
Machine learning models learn to predict new values or classes through training and learning on data that is already available. The data contains the values needed for prediction.
Together, statistics and probability form the backbone of AI, the technology that will drive the future. A strong understanding of mathematics in school prepares students for advanced topics in AI and machine learning
