This course provides a solid foundation in the mathematical concepts essential for understanding and implementing machine learning algorithms. It begins with the fundamentals of vectors, matrices, and linear systems, which are crucial for data representation and manipulation. The course then explores differential calculus and partial derivatives, key for optimization techniques used in machine learning. Participants will also learn about optimization methods such as gradient descent, which is central to many machine learning algorithms. Additionally, the course covers regularization and cost functions, helping learners understand how to improve model performance and avoid overfitting. Throughout, the course emphasizes practical applications and provides examples to solidify understanding.

• Vectors, Matrices, and Linear Systems: Introduction to vectors and matrices, basic operations, and solving linear systems. Applications to data representation and manipulation
• Differential Calculus and Partial Derivatives: Basic principles of differential calculus, focusing on partial derivatives, gradients, and their use in optimization
• Optimization Techniques: Overview of optimization methods, with a focus on gradient descent and its role in machine learning models
• Regularization and Cost Functions: Introduction to regularization techniques to prevent overfitting, and understanding cost functions for model evaluation and improvement

By the end of this course, participants will be able to: 

• Understand and apply the basic concepts of vectors, matrices, and linear systems in machine learning
• Compute and interpret partial derivatives and gradients for optimization tasks
• Apply optimization techniques, including gradient descent, to minimize cost functions
• Recognize and implement regularization techniques to improve model generalization and prevent overfitttng

The course follows a traditional classroom format with slide presentations, fostering interactive engagement and active participation.