Maths for AI Course Overview

Maths for AI Course Overview

The Maths for AI course provides a comprehensive curriculum designed to equip learners with the mathematical foundation necessary for understanding and advancing in the field of artificial intelligence. This course covers a broad spectrum of topics, from linear algebra and calculus to probability, statistics, and optimization techniques.

Starting with Module 1, students dive into linear algebra, which is critical for AI applications such as computer vision and machine learning. They learn about vectors, matrices, and operations that are essential for representing and manipulating data.

In Module 2, the course delves into calculus, which underpins the optimization algorithms like gradient descent used in training machine learning models.

Module 3's focus on probability and statistics lays the groundwork for making predictions and decisions under uncertainty, a common scenario in AI.

Modules 4 and 5 introduce mathematical reasoning and optimization for AI, enhancing learners' abilities to approach complex problems and improve AI algorithms.

Lastly, Modules 6 and 7 cover linear regression, regularization, and neural networks, providing practical skills for designing and evaluating AI models.

Overall, this course is tailored to impart the robust mathematical skills necessary for AI, with an emphasis on practical applications that will empower learners to excel in the cutting-edge field of artificial intelligence.

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  • Live Online Training (Duration : 36 Hours)
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  • Live Online Training (Duration : 36 Hours)
  • Per Participant

♱ Excluding VAT/GST

Classroom Training price is on request

You can request classroom training in any city on any date by Requesting More Information

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Course Prerequisites

To successfully undertake the Maths for AI course, it is essential that students have a foundational understanding of certain mathematical concepts. Here are the minimum required prerequisites:

  • Basic knowledge of high school mathematics, including algebra and geometry.
  • Familiarity with trigonometric functions and their properties.
  • Understanding of fundamental concepts of functions and graphs.
  • Comfort with mathematical notation and the ability to follow mathematical arguments.
  • Basic problem-solving skills and logical reasoning abilities.
  • Willingness to learn and apply new mathematical concepts specific to artificial intelligence.

By meeting these prerequisites, students will be well-equipped to grasp the course material and apply it to AI-related problems. Please note that while prior exposure to advanced mathematics can be beneficial, the course is designed to build up the necessary skills from the basics, ensuring that all students with a solid foundational knowledge have the opportunity to succeed.

Target Audience for Maths for AI

  1. "Maths for AI" is a comprehensive course designed for individuals looking to deepen their understanding of mathematical concepts essential for AI and ML technologies.

  2. Target audience for "Maths for AI" includes:

  • Aspiring and current Data Scientists
  • Machine Learning Engineers
  • AI Research Scientists
  • Software Engineers interested in AI applications
  • Students pursuing degrees in Computer Science, Data Science, or AI
  • Statisticians seeking to apply their expertise in AI
  • Quantitative Analysts transitioning to AI roles
  • Professionals in tech roles looking to upskill in AI and ML
  • Academic Researchers in mathematics or computer science
  • Technology Consultants focusing on AI solutions
  • AI Product Managers
  • Developers working on AI-powered tools and applications

Learning Objectives - What you will Learn in this Maths for AI?

Introduction to the Course's Learning Outcomes and Concepts Covered

This Maths for AI course equips students with the foundational mathematical knowledge essential for understanding and creating AI algorithms, spanning linear algebra to neural network principles.

Learning Objectives and Outcomes

  • Comprehend basic algebra, functions, and how they apply to AI.
  • Understand and manipulate scalars, vectors, and matrices, essential elements in AI algorithms.
  • Perform matrix operations and understand the concepts of linear independence, basis, and dimension.
  • Calculate eigenvalues and eigenvectors, crucial for dimensionality reduction and data simplification in AI.
  • Apply calculus, including limits, derivatives, and integrals, for optimizing AI models.
  • Utilize optimization techniques and apply multivariable calculus for improving AI algorithms.
  • Grasp probability theory and statistics to model uncertainties and make predictions in AI.
  • Employ mathematical reasoning tools such as set theory, graph theory, and decision theory for AI problem-solving.
  • Develop and refine AI models using optimization methods like gradient descent and regularization techniques.
  • Implement and analyze neural network architectures, including CNNs and RNNs, for advanced AI applications.