## Foundational Mathematics for AI and Data Science

Preference Dates Timing Location Registration Fees
Instructor-Led Training

(In-Person and Live Webinars)
4, 5, 11,12, 18, 19, 25, 26 November 2023 Sat. & Sun.: 4:00 PM - 6:30 PM Dubai Knowledge Park 1225 USD

### Course Description

This weekend-intensive course is designed to equip learners with the fundamental mathematical concepts crucial for artificial intelligence and data science. Covering linear algebra, probability and statistics, and calculus, participants will gain a comprehensive understanding of the mathematical theories that power modern AI and data analytics tools.

A significant component of the course is dedicated to practical labs using Python, where students will apply these mathematical concepts to real-world problems. By the end of the course, not only will students be proficient in understanding these theoretical foundations, but they’ll also be adept at translating them into tangible, practical applications using Python.   Upon successful completion of this program, the participants will earn a KHDA-Accredited AI Certificate.

Course Modules

### Module 1: Linear Algebra for Machine Learning

• Introduction to Linear Algebra: Understand the definition and significance of linear algebra in the realm of machine learning.
• Vectors and Matrices: Dive into operations, properties, and the geometric interpretations of these fundamental constructs.
• Matrix Decompositions: Grasp the concepts of eigenvalues, eigenvectors, and the singular value decomposition and their roles in machine learning.
• Linear Transformations: Explore different types of linear transformations and their applications in data manipulation and interpretation.
• Orthogonality and Least Squares: Discover the world of orthogonal vectors, projection techniques, and their pivotal role in regression analysis.

### Module 2: Probability and Statistics for Data Science

• Basics of Probability: Delve into the foundational concepts, including definitions, axioms, conditional probability, and the renowned Bayes’ theorem.
• Random Variables and Distributions: Uncover the intricacies of expectation, variance, and common statistical distributions like the binomial and normal.
• Sampling and Estimations: Understand the Central Limit Theorem, the Law of Large Numbers, and the art of constructing confidence intervals.
• Hypothesis Testing: Learn about p-values, type I and II errors, and various testing methods like the t-test and chi-square test.
• Correlation and Regression: Discern the relationships between variables and dive deep into both simple and multiple regression techniques.

### Module 3: Calculus for Machine Learning

• Introduction to Calculus: Recognize the paramount importance of calculus in optimization tasks and in dissecting machine learning algorithms.
• Limits and Continuity: Establish a foundational understanding of the bedrock concepts of calculus.
• Differentiation: Grasp differentiation rules, partial derivatives, and their indispensable application in optimization techniques like gradient descent.
• Integration: Understand the fundamental theorem of calculus, various integration techniques, and their relevance in computing areas under curves.
• Multivariable Calculus: Delve into advanced topics like gradients, Hessians, and their pivotal roles in optimization.

### Project Work

To ensure the learning journey comes full circle, students will be given the opportunity to engage in project work using Python during the course. This hands-on approach will solidify the theoretical concepts covered and showcase their practical applications.

#### Target Audience

• Individuals new to the field of AI and data science, seeking a foundational understanding of its core mathematical concepts.
• Students and professionals who want to delve into the mathematical underpinnings of AI and data science.
• Data science enthusiasts keen on translating theoretical mathematical concepts into practical Python implementations.
• AI practitioners eager to strengthen their foundational understanding of the mathematical constructs powering their models.
• Computer Science students looking to supplement their coursework with applicable mathematics for AI.
• Professionals in fields such as engineering, physics, or finance, aiming to transition into data science roles.
• Researchers who intend to use AI and machine learning methods in their projects and need a foundational understanding of the core mathematics.

#### Prerequisites

• A foundational understanding of high school level mathematics (especially algebra).
• Curiosity about the mathematical principles that underpin AI and data science.
• Access to a computer with a modern web browser for any computational components of the course.
• A willingness to engage with mathematical proofs and concepts in depth.
• Basic understanding of programming concepts (beneficial for applied sections, but not mandatory).
• Enthusiasm to dive deep into the intricacies of mathematical models and their relation to real-world applications.

After the Course

• Understand and apply the foundational mathematical concepts underpinning AI and data science, such as linear algebra, calculus, and probability.
• Translate complex mathematical theories into actionable insights using Python for AI and data science tasks.
• Utilize Python to apply matrix operations, statistical analyses, and optimization algorithms in real-world problems.
• Engage confidently in discussions and projects related to AI and data science, showcasing your understanding of both the theoretical and practical aspects.
• Successfully embark on more advanced AI or data science courses, research projects, or job roles, with a solid foundation to build upon.
• Gain recognition and credibility in the AI and data science community with the KHDA-Accredited AI Certificate.
• Connect and network with like-minded peers and professionals, opening doors to collaborative opportunities and career advancements.