OPTIMIZE-ML.AU1
First-order and Stochastic Optimization Methods for Machine Learning
Master the mathematical foundations of first-order and stochastic optimization to build faster, scalable, and high-performance machine learning models for large-scale data.
- Practice in 22 Hands-On Labs — nothing to install
- 8 Interactive Lessons and 52 topics mapped to the official exam objectives
Intermediate Self-paced · 1 year access
22 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
01 / Skills you'll get
What you will be able to do
You know the algorithms—linear regression, support vector machines, neural networks—but do you know what truly powers their performance and speed? It's optimization.
This is not just another machine learning course. This intensive program dives deep into the mathematical and algorithmic core of first-order optimization methods. As datasets explode in size, traditional batch methods fail. This course is designed to equip you with the specialized knowledge to thrive in the era of large-scale machine learning by mastering stochastic optimization methods.
If you are a machine learning researcher, data scientist, or engineer serious about developing faster, more scalable, and mathematically sound AI models, this is your next step.
Upon completion of this course and its hands-on LAB activities, you will be able to:
- Implement Advanced Model Generalization: Apply foundational models (LR, SVM, NNs) and master essential regularization techniques (Lasso and Ridge) to build models with superior out-of-sample performance.
- Establish Algorithmic Foundations: Master the theory of Convex Optimization, including Convex Sets, Convex Functions, and Lagrangian and Legendre–Fenchel Duality, forming a basis for rigorous algorithm design.
- Drive Faster Convergence: Analyze and implement core First-Order Optimization algorithms—from Subgradient Descent to sophisticated Accelerated Gradient Descent methods and the powerful Primal–Dual Method—and perform their quantitative Convergence Analysis.
- Scale Optimization for Big Data: Design and deploy modern Stochastic Optimization Methods and Variance-Reduced algorithms to efficiently solve Nonconvex Optimization problems and manage large-scale and Distributed Optimization environments.
Course Highlights
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8 Structured Lessons Comprehensive coverage of core course objectives
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22 Hands-On LiveLabs Interactive guided scenarios with instant evaluation
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1 Year Full Access Self-paced learning accessible anytime on all devices
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
8 Interactive Lessons · 52 topics01 Regularization Techniques for Generalization 8 topics · 4 LiveLab +
- Linear Regression
- Logistic Regression
- Generalized Linear Models
- Support Vector Machines
- Regularization, Lasso, and Ridge Regression
- Population Risk Minimization
- Neural Networks
- Exercises
4 LiveLab in this lesson — see the labs panel →
02 Convergence Analysis of Optimization Algorithms 5 topics · 3 LiveLab +
- Convex Sets
- Convex Functions
- Lagrange Duality
- Legendre–Fenchel Conjugate Duality
- Exercises
3 LiveLab in this lesson — see the labs panel →
03 Deterministic Convex Optimization 10 topics · 1 LiveLab +
- Subgradient Descent
- Mirror Descent
- Accelerated Gradient Descent
- Game Interpretation for Accelerated Gradient Descent
- Smoothing Scheme for Nonsmooth Problems
- Primal–Dual Method for Saddle-Point Optimization
- Alternating Direction Method of Multipliers
- Mirror-Prox Method for Variational Inequalities
- Accelerated Level Method
- Exercises
1 LiveLab in this lesson — see the labs panel →
04 Stochastic Convex Optimization 7 topics · 3 LiveLab +
- Stochastic Mirror Descent
- Stochastic Accelerated Gradient Descent
- Stochastic Convex–Concave Saddle Point Problems
- Stochastic Accelerated Primal–Dual Method
- Stochastic Accelerated Mirror-Prox Method
- Stochastic Block Mirror Descent Method
- Exercises
3 LiveLab in this lesson — see the labs panel →
05 Convex Finite-Sum and Distributed Optimization 5 topics · 3 LiveLab +
- Random Primal–Dual Gradient Method
- Random Gradient Extrapolation Method
- Variance-Reduced Mirror Descent
- Variance-Reduced Accelerated Gradient Descent
- Exercises
3 LiveLab in this lesson — see the labs panel →
06 Nonconvex Optimization 7 topics · 3 LiveLab +
- Unconstrained Nonconvex Stochastic Optimization
- Nonconvex Stochastic Composite Optimization
- Nonconvex Stochastic Block Mirror Descent
- Nonconvex Stochastic Accelerated Gradient Descent
- Nonconvex Variance-Reduced Mirror Descent
- Randomized Accelerated Proximal-Point Methods
- Exercises
3 LiveLab in this lesson — see the labs panel →
07 Advanced Gradient-Based Optimization 6 topics · 3 LiveLab +
- Conditional Gradient Method
- Conditional Gradient Sliding Method
- Nonconvex Conditional Gradient Method
- Stochastic Nonconvex Conditional Gradient
- Stochastic Nonconvex Conditional Gradient Sliding
- Exercises
3 LiveLab in this lesson — see the labs panel →
08 Operator Sliding and Decentralized Optimization 4 topics · 2 LiveLab +
- Gradient Sliding for Composite Optimization
- Accelerated Gradient Sliding
- Communication Sliding and Decentralized Optimization
- Exercises
2 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
22 LiveLabs- Performing Linear Regression Using OLS
- Performing Logistic Regression for Binary Classification
- Performing Classification Using SVM
- Training a Neural Network Using the Adam Optimizer
- Exploring and Visualizing Convex Sets Using Python
- Analyzing and Visualizing Convex Functions with Python
- Visualizing Legendre-Fenchel Conjugate Duality
- Comparing the Convergence of Optimizers on a Loss Landscape
- Applying SMD on a Convex Function
- Implementing the SAGD Algorithm
- Optimizing Stochastic Convex–Concave Saddle Points
- Improving Model Performance with Regularization
- Implementing the RPDG Method on Distributed Data
- Simulating RGE for Multi-Worker Training
- Solving Convex and Non-Convex Optimization Problems
- Implementing Nonconvex Stochastic Optimization
- Comparing Nonconvex Mirror Descent and Accelerated Gradient Descent
- Implementing Conditional Gradient Algorithm
- Implementing the SCG Algorithm
- Fine-Tuning a Pretrained Model with Advanced Optimizers
- Simulating Communication-Efficient Distributed Optimization
- Applying Gradient Sliding for Composite Convex Optimization
03 / FAQs
Questions before you start
Who should take this course?+
Why is knowing optimization theory critical for Machine Learning?+
Does this course cover deep learning optimizers like Adam and RMSProp?+
Is there a focus on large-scale or distributed problems?+
Keep going
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Ready to Elevate Your ML Expertise?
Enroll Today! Master the foundational and advanced techniques in First-Order Optimization to build the next generation of machine learning systems.
- 1 year of full access
- 22 LiveLab included
- Certificate of completion
No credit card required