Mathematics of Optimization in ML

A rigorous, intuition-driven exploration of the mathematical foundations and optimization algorithms that power modern machine learning. This course blends theory, geometric intuition, and Python-based visualizations to build a deep understanding of how optimization works in ML.Show more

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18 chapters

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Learning outcomes

Modules

Testimonials

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Codefinity advantages

FAQs

Outcomes

Learning outcomes

Modules

Testimonials

Recommendations

Codefinity advantages

FAQs

What you will learn

Understand derivatives, gradients, and convexity in the context of optimization objectives.
Interpret the geometric meaning of loss functions and visualize convex/non-convex surfaces.
Derive and analyze gradient descent and its convergence properties.
Comprehend stochastic and mini-batch optimization, including the role of noise and variance.

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Assessments

18 assignments

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There are 6 modules in this course

Core mathematical concepts underlying optimization in machine learning, including derivatives, gradients, convexity, and the geometry of loss functions.

Mathematical derivation and practical intuition for gradient descent, including the role of learning rate and stepwise visualization.

Explore stochastic optimization, expectation and variance, and the impact of noise and batch size on convergence.

Mathematical and visual exploration of momentum-based optimization and acceleration techniques.

In-depth study of adaptive optimization algorithms, their mathematical foundations, and visual behavior.

Explore convergence proofs, learning rate schedules, and challenges in non-convex optimization.

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Kwizera Mugisha

The teaching methodology at Codefinity is excellent, and I particularly appreciate how it has prepared me to handle real-world coding problems. Currently, I am delving into Node.js and eagerly anticipate building full-stack projects that integrate all the knowledge I have gained.

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Bill Wagner

I have really liked the browser-based lessons that allow me to code within the lesson. The RUN button allows me to test the code I write before submitting for a grade.

Stephanie Chan

As I went through the first course of the Python track, I liked the way the course was lay out (in easy and digestible modules) with little exercises at the end of each concept.

Daniel Chinea

I have gained a lot of practical and logical thinking skills, along with patience for myself and confidence in myself that I can learn programming.

Steve Bruening

The learning was progressive and made it easy to follow along and make progress. I could feel my skills increasing and building on each other as the course went along.

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Introduction to Neural Networks

ML Introduction with scikit-learn

Introduction to NLP

Learning Statistics with Python

Classification with Python

Cluster Analysis

Introduction to RNNs

Mathematics for Data Science

Matlab Basics

Explainable AI (XAI) Basics

Introduction to Reinforcement Learning

PyTorch Essentials

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Your subscription also includes:

Introduction to Neural Networks

ML Introduction with scikit-learn

Introduction to NLP

Learning Statistics with Python

Classification with Python

Cluster Analysis

Introduction to RNNs

Mathematics for Data Science

Matlab Basics

Explainable AI (XAI) Basics

Introduction to Reinforcement Learning

PyTorch Essentials

Introduction to Neural Networks

ML Introduction with scikit-learn

Introduction to NLP

Learning Statistics with Python

Classification with Python

Cluster Analysis

Introduction to RNNs

Mathematics for Data Science

Matlab Basics

Explainable AI (XAI) Basics

Introduction to Reinforcement Learning

PyTorch Essentials

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