Mathematics for Predictive Analytics/ Foundations of AI, ML & Data Science by Dr C S K Raju.

How does mathematics power modern predictive analytics, machine learning, and artificial intelligence? This session explores the mathematical foundations behind data-driven prediction, intelligent algorithms, and computational models. Based on the topics presented in the uploaded material, the session connects linear algebra, probability, statistics, calculus, numerical methods, optimization, machine learning, and artificial neural networks with practical applications in science, engineering, data analytics, and emerging technologies. The session explains how mathematical concepts enable models to learn from data, quantify uncertainty, identify patterns, reduce dimensionality, optimize predictions, and support reliable decision-making. 📌 Topics Covered 🔹 Mathematics behind Predictive Analytics 🔹 Role of Mathematics in AI and Machine Learning 🔹 Linear Algebra as the Core Computational Engine 🔹 Vectors, Matrices, Matrix Multiplication, Rank and Transformations 🔹 Eigenvalues and Eigenvectors 🔹 Principal Component Analysis (PCA) 🔹 Dimensionality Reduction and Data Representation 🔹 Supervised, Unsupervised and Reinforcement Learning 🔹 Linear and Polynomial Regression 🔹 Logistic Regression and Classification 🔹 Support Vector Machines (SVM) 🔹 Artificial Neural Networks (ANN) 🔹 Deep Learning concepts including CNN and RNN 🔹 K-Means Clustering and Anomaly Detection 🔹 Network Theory and Social Media Influence using Adjacency Matrices 🔹 Linear Systems of Equations and Engineering Applications 🔹 Electrical Circuit Analysis using Kirchhoff’s Laws 🔹 Linear Transformations in Cryptography and Image Processing 🔹 Matrix-Based Encryption and Decryption 🔹 Cayley–Hamilton Theorem and its Applications 🔹 Probability and Statistics for Uncertainty Modelling 🔹 Calculus for Learning and Optimization 🔹 Numerical Methods for Computational Solutions 🔹 Information Theory and Efficient Learning 🔹 Stochastic Processes for Time-Based Prediction 🔹 Differential Equations and Physics-Constrained Models 🔹 Practical computational examples and mathematical applications This session is designed to bridge the gap between mathematical theory and modern predictive technologies, showing how core mathematical ideas form the foundation of machine learning algorithms, artificial intelligence systems, data science techniques, and real-world computational models. 🎯 Who can benefit from this video? Undergraduate and postgraduate students, research scholars, faculty members, data science learners, AI and ML enthusiasts, engineering students, mathematics students, and researchers interested in predictive modelling and computational intelligence. By the end of this video, viewers will gain a clearer understanding of how mathematics acts as the backbone of predictive analytics and how concepts such as matrices, probability, optimization, regression, PCA, and neural networks contribute to intelligent prediction and data-driven decision-making. Presented by: Dr. Chakravarthula S K Raju #MathematicsForPredictiveAnalytics #PredictiveAnalytics #Mathematics #ArtificialIntelligence #MachineLearning #DataScience #LinearAlgebra #PCA #NeuralNetworks #ANN #DeepLearning #Regression #Statistics #Probability #Optimization #NumericalMethods #SVM #KMeans #AI #ML #AppliedMathematics #MathematicalModeling