CTU-AI345.AU1
Optimization Methods for AI
- Practice in 20 Hands-On Labs — nothing to install
- 5 Interactive Lessons and 44 topics mapped to the official exam objectives
Self-paced · 1 year access
20 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
5Interactive Lessons
44Topics
20LiveLab
81Flashcards
81Glossary of terms
01 / Lessons & labs
See exactly what you will learn and practice
Lessons
5 Interactive Lessons · 44 topics01 Regularization Techniques for Generalization 7 topics · 4 LiveLab +
- Linear Regression
- Logistic Regression
- Generalized Linear Models
- Support Vector Machines
- Regularization, Lasso, and Ridge Regression
- Population Risk Minimization
- Neural Networks
4 LiveLab in this lesson — see the labs panel →
02 Gradient Descent and Its Variants 15 topics · 4 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
- 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
4 LiveLab in this lesson — see the labs panel →
03 Convergence Analysis of Optimization Algorithms 11 topics · 6 LiveLab +
- Convex Sets
- Convex Functions
- Lagrange Duality
- Legendre–Fenchel Conjugate Duality
- Unconstrained Nonconvex Stochastic Optimization
- Unconstrained Nonconvex Stochastic Optimization Part B
- Nonconvex Stochastic Composite Optimization
- Nonconvex Stochastic Block Mirror Descent
- Nonconvex Stochastic Accelerated Gradient Descent
- Nonconvex Variance-Reduced Mirror Descent
- Randomized Accelerated Proximal-Point Methods
6 LiveLab in this lesson — see the labs panel →
04 Convex vs. Non-Convex Optimization in AI 6 topics · 3 LiveLab +
- Random Primal–Dual Gradient Method
- Random Primal–Dual Gradient Method Part B
- Random Gradient Extrapolation Method
- Random Gradient Extrapolation Method Part B
- Variance-Reduced Mirror Descent
- Variance-Reduced Accelerated Gradient Descent
3 LiveLab in this lesson — see the labs panel →
05 Advanced Gradient-Based Optimization 5 topics · 3 LiveLab +
- Conditional Gradient Method
- Conditional Gradient Sliding Method
- Nonconvex Conditional Gradient Method
- Stochastic Nonconvex Conditional Gradient
- Stochastic Nonconvex Conditional Gradient Sliding
3 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
20 LiveLabs- Performing Linear Regression Using OLS
- Performing Logistic Regression for Binary Classification
- Performing Classification Using SVM
- Training a Neural Network Using the Adam Optimizer
- 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
- Exploring and Visualizing Convex Sets Using Python
- Analyzing and Visualizing Convex Functions with Python
- Visualizing Legendre-Fenchel Conjugate Duality
- Solving Convex and Non-Convex Optimization Problems
- Implementing Nonconvex Stochastic Optimization
- Comparing Nonconvex Mirror Descent and Accelerated Gradient Descent
- Improving Model Performance with Regularization
- Implementing the RPDG Method on Distributed Data
- Simulating RGE for Multi-Worker Training
- Implementing Conditional Gradient Algorithm
- Implementing the SCG Algorithm
- Fine-Tuning a Pretrained Model with Advanced Optimizers
Labs run in your browser — nothing to install.
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- 1 year of full access
- 20 LiveLab included
- Certificate of completion