Mathematical Foundations of Machine Learning

What Will You Learn?

• Understand the fundamentals of linear algebra and calculus, critical mathematical subjects underlying all of machine learning and data science
• Manipulate tensors using all three of the most important Python tensor libraries: NumPy, TensorFlow, and PyTorch
• How to apply all of the essential vector and matrix operations for machine learning and data science
• Reduce the dimensionality of complex data to the most informative elements with eigenvectors, SVD, and PCA
• Solve for unknowns with both simple techniques (e.g., elimination) and advanced techniques (e.g., pseudoinversion)
• Appreciate how calculus works, from first principles, via interactive code demos in Python
• Intimately understand advanced differentiation rules like the chain rule
• Compute the partial derivatives of machine-learning cost functions by hand as well as with TensorFlow and PyTorch
• Grasp exactly what gradients are and appreciate why they are essential for enabling ML via gradient descent
• Use integral calculus to determine the area under any given curve
• Be able to more intimately grasp the details of cutting-edge machine learning papers
• Develop an understanding of what’s going on beneath the hood of machine learning algorithms, including those used for deep learning