Program Overview

Mathematical Sciences for Artificial Intelligence

The Mathematical Sciences for Artificial Intelligence program is designed to provide students with a comprehensive understanding of the mathematical foundations of artificial intelligence. The program aims to equip students with the skills and knowledge necessary to tackle complex problems in artificial intelligence, including machine learning, data analysis, and computational modeling.

Curriculum

The curriculum for the Mathematical Sciences for Artificial Intelligence program is divided into several courses, each with its own set of educational objectives. The courses are designed to provide students with a deep understanding of the mathematical concepts and techniques used in artificial intelligence.

• Linear Algebra and Algebraic Structures: This course provides an overview of the main structures used in artificial intelligence, including vector spaces, matrices, and groups. Students will learn how to manipulate these structures and apply them to solve problems in artificial intelligence.
• Three-Dimensional Modeling: This course introduces students to the elementary concepts of algebra and provides an introduction to the main algebraic structures used in artificial intelligence.
• Mathematical Analysis I: This course provides a rigorous introduction to mathematical analysis, including the study of functions, limits, and calculus. Students will learn how to apply these concepts to solve problems in artificial intelligence.
• Programming Fundamentals with Laboratory: This course introduces students to programming using the Python language. Students will learn how to design and implement algorithms, as well as how to use programming to solve problems in artificial intelligence.
• Physics: This course provides an introduction to the main concepts of physics, including mechanics, thermodynamics, electromagnetism, and optics. Students will learn how to apply these concepts to solve problems in artificial intelligence.
• Programming Techniques with Laboratory: This course provides an introduction to computational thinking and problem-solving using Python programming. Students will learn how to design and implement algorithms, as well as how to use programming to solve problems in artificial intelligence.
• Algorithms and Complexity: This course provides an introduction to the design and analysis of algorithms, including the study of computational complexity. Students will learn how to design and implement efficient algorithms to solve problems in artificial intelligence.
• Mathematical Modelling for Physics: This course provides an introduction to mathematical modeling for physics, including the study of classical mechanics and dynamical systems. Students will learn how to apply these concepts to solve problems in artificial intelligence.
• Numerical Methods: This course provides an introduction to numerical methods for solving mathematical problems, including the study of linear systems, approximation methods, and quadrature techniques. Students will learn how to apply these concepts to solve problems in artificial intelligence.
• Fundamentals of Artificial Intelligence and Data Management: This course provides an introduction to the fundamental methods of artificial intelligence, including machine learning, data analysis, and computational modeling. Students will learn how to apply these concepts to solve problems in artificial intelligence.
• Machine Learning: This course provides an introduction to machine learning, including the study of supervised and unsupervised learning, clustering algorithms, and deep neural networks. Students will learn how to apply these concepts to solve problems in artificial intelligence.
• Optimization: This course provides an introduction to optimization techniques, including the study of linear and nonlinear programming. Students will learn how to apply these concepts to solve problems in artificial intelligence.
• Computational Biology: This course provides an introduction to computational biology, including the study of bioinformatics, genomics, and proteomics. Students will learn how to apply these concepts to solve problems in artificial intelligence.
• Stochastic Processes: This course provides an introduction to stochastic processes, including the study of Markov chains, Brownian motion, and Monte Carlo methods. Students will learn how to apply these concepts to solve problems in artificial intelligence.

Elective Courses

Students can choose from a range of elective courses, including:

• Earthquake Physics and Machine Learning: This course provides an introduction to earthquake physics and machine learning, including the study of seismic data analysis and earthquake prediction.
• Computer Architectures for Artificial Intelligence: This course provides an introduction to computer architectures for artificial intelligence, including the study of hardware solutions for AI and programming paradigms.
• Scientific Computing: This course provides an introduction to scientific computing, including the study of numerical methods for solving mathematical problems and programming techniques.
• Artificial Intelligence Methods for Physics: This course provides an introduction to artificial intelligence methods for physics, including the study of machine learning and data analysis techniques.
• Applications of Machine Learning in Computer Science: This course provides an introduction to applications of machine learning in computer science, including the study of natural language processing, artificial vision, and recommendation systems.
• Mathematics for Machine Learning: This course provides an introduction to mathematics for machine learning, including the study of statistical learning theory and machine learning algorithms.
• Logic and Probabilistic Methods for Computer Science: This course provides an introduction to logic and probabilistic methods for computer science, including the study of mathematical logic and probabilistic algorithms.

Final Exam

The final exam is a comprehensive assessment of the student's knowledge and skills in mathematical sciences for artificial intelligence. The exam includes a written paper and an oral presentation, and is designed to test the student's ability to apply mathematical concepts and techniques to solve problems in artificial intelligence.