Mathematics for Machine Learning Specialization Course Syllabus

Overview: This specialization provides a comprehensive introduction to the core mathematical concepts behind machine learning, designed for beginners with basic math knowledge. The course is divided into four key modules covering linear algebra, multivariable calculus, probability and statistics, and a capstone project. With a total time commitment of approximately 24–36 weeks at a pace of 4–7 hours per week, learners will build a solid foundation in the mathematics essential for AI and machine learning. Each module combines theory with hands-on exercises to reinforce understanding and application.

Module 1: Linear Algebra for Machine Learning

Estimated time: 35 hours

• Vectors and vector spaces
• Matrix operations and properties
• Linear transformations
• Eigenvalues and eigenvectors and their applications in ML

Module 2: Multivariable Calculus for Machine Learning

Estimated time: 50 hours

• Differentiation of functions with multiple variables
• Partial derivatives and gradients
• Gradient-based optimization techniques
• Backpropagation in neural networks

Module 3: Probability and Statistics for Machine Learning

Estimated time: 70 hours

• Probability distributions and Bayes’ theorem
• Statistical inference and data analysis
• Hypothesis testing and confidence intervals
• Markov Chains and their machine learning applications

Module 4: Mathematical Modeling for Machine Learning

Estimated time: 40 hours

• Building mathematical models for real-world problems
• Integrating linear algebra and calculus in ML contexts
• Using probability for decision-making in uncertain environments

Module 5: Optimization and Applications in ML

Estimated time: 30 hours

• Cost functions and parameter optimization
• Applications of gradient descent in ML algorithms
• Mathematical foundations of deep learning models

Module 6: Final Project

Estimated time: 100 hours

• Design and implement a machine learning model using core mathematical principles
• Apply linear algebra, calculus, and probability to optimize model performance
• Submit a case study report with code and mathematical analysis

Prerequisites

• Basic algebra and high school-level mathematics
• Familiarity with introductory calculus concepts
• Some exposure to programming (helpful but not required)

What You'll Be Able to Do After

• Understand and apply core linear algebra concepts in machine learning
• Use multivariable calculus to optimize learning algorithms
• Analyze data using probability and statistical inference
• Build mathematical models for real-world AI applications
• Solve practical machine learning problems using foundational math