In this course, you will learn about the mathematical concepts that underpin AI and machine learning. This is essential for understanding the mathematical approaches and techniques used in AI. Consequently, the knowledge gained will allow you to develop, analyze, and optimize AI models.

By the end of this course, you should be able to:

CLO1. Analyze the fundamental concepts of linear algebra, calculus, and statistics as they apply to artificial intelligence

CLO2. Evaluate the effectiveness of various mathematical techniques in solving AI-related problems.

CLO3. Apply optimization methods to enhance the performance of AI algorithms.

CLO4. Synthesize knowledge from linear algebra, calculus, and statistics to develop and improve AI models

Linear Algebra: Vectors and vector spaces, Matrix operations, Linear transformations.
Eigenvalues and Eigenvectors: Eigenvalues and eigenvectors, Diagonalization, Applications in AI;
Singular Value Decomposition (SVD): SVD theory, Applications in data compression and noise reduction.
Calculus: Limits and continuity, Differentiation, Integration;
Multivariable Calculus: Partial derivatives, Gradient, divergence, and curl, Multiple integrals;
Optimization Techniques: Gradient descent, Constrained optimization, Lagrange multipliers
Building Consensus,Commitment and Cooperation: Principles of Ethical Leadership; Ethical Dilemmas faced by Leaders; Techniques for Building Consensus; Securing Commitment and Cooperation to Change Initiatives;
Probability and Statistics: Probability theory, Random variables, Probability distributions;
Statistical Inference: Estimation and Hypothesis testing, Confidence intervals, p-values;
Regression Analysis:  Linear regression, Logistic regression, Applications in AI.