Advanced Mathematics for Machine Learning Course Overview

Course Prerequisites

Target Audience for Advanced Mathematics for Machine Learning

Advanced Mathematics for Machine Learning is a comprehensive 5-day course designed for professionals seeking to deepen their mathematical understanding essential for advanced machine learning applications and algorithms.

Target Audience and Job Roles:

• Data Scientists
• Machine Learning Engineers
• AI Researchers
• Statisticians
• Quantitative Analysts
• Software Engineers specializing in AI/ML
• PhD Students in Computer Science or Data Science
• Research Scientists
• Algorithm Engineers
• Mathematical Modelers
• Academicians in Mathematics, Statistics, or Machine Learning
• Operations Research Analysts
• Business Intelligence Analysts
• Computer Vision Engineers
• Natural Language Processing Engineers

Learning Objectives - What you will Learn in this Advanced Mathematics for Machine Learning?

Introduction: The Advanced Mathematics for Machine Learning course at Koenig Solutions is a comprehensive 5-day training program designed to equip students with essential mathematical tools and techniques for advanced machine learning applications, covering topics from analytic geometry to support vector machines.

Learning Objectives and Outcomes:

• Understand and apply norms, inner products, and orthogonality in analytic geometry.
• Perform and interpret various matrix decompositions, including Cholesky, eigen, and singular value decompositions.
• Gain proficiency in backpropagation and automatic differentiation for neural networks.
• Explore advanced optimization techniques, including constrained optimization, convex optimization, and optimization in high dimensions.
• Conduct dimensionality reduction using Principal Component Analysis (PCA) from multiple perspectives.
• Master density estimation using Gaussian Mixture Models (GMM) and EM algorithms.
• Classify data with support vector machines (SVM) and understand the use of kernels and numerical solutions.

This structured approach ensures a strong mathematical foundation for students, enabling them to tackle complex machine learning problems efficiently.