APMTH 207 - Advanced Scientific Computing: Stochastic Methods for Data Analysis, Inference and Optimization Learning Course

[APMTH 207 - Advanced Scientific Computing: Stochastic Methods for Data Analysis, Inference and Optimization Learning Course] APMTH 207 - Advanced Scientific Computing: Stochastic Methods for Data Analysis, Inference and Optimization: APMTH 207 explores advanced stochastic methods for analyzing and optimizing data-driven models in complex systems. Topics include Bayesian inference, Monte Carlo methods, stochastic gradient descent, and probabilistic graphical models. The course emphasizes applications in machine learning, computational statistics, and optimization problems in large-scale data analysis. Through theoretical study and practical assignments using computational tools, students gain expertise in leveraging stochastic methods to extract insights from data, make informed decisions, and optimize system performance. The course prepares students for research and professional roles requiring advanced computational and statistical skills in data-driven disciplines. This Learning Course is a platform that helps students to study and learn. The platform provides all the required material for the students to study. The material includes Study guide, Exam Questions & Answers, Study Notes and other resources that are useful for the students. It also gives information about the upcoming exams so that students can prepare themselves accordingly.