Introduction to Machine Learning and Mathematical Foundations Linear Algebra: - Vectors, Matrices, and Tensors - Eigenvalues, Eigenvectors, and Matrix Factorization - Applications of Linear Algebra in Machine Learning - Basic implementation of matrix operations in machine learning algorithms Probability Theory and Statistical Concepts: - Introduction to Probability Theory (Random Variables, Distributions) - Statistical Measures (Mean, Variance, Covariance, Correlation) - Bayes’ Theorem and its importance in ML Linear and Logistic Regression: - Mathematics of Linear Regression (Least Squares, Gradient Descent) - Introduction to Logistic Regression for Binary Classification Speaker(s): Dr. Pratik Barot, Dr. Hiten Kanani, Dr. Parita Shah, Dr. Krunal Kachhia, Dr. Mrugendrasinh Rahevar, Dr. Brajeshkumar Jha, Dr. Safvan Vahora, Dr. Tathagatha Bandyopadhyay, Dr. Ojas Shriniwas, Prof. Manoj Sahni Room: Auditorium Hall, 3rd Floor, Bldg: Biotechnology / Microbiology Department, LDRP Institute of Technology & Research Campus, Near KH-5 Circle, Gandhinagar, Gujarat, India, 382016