Master Advanced Machine Learning: From PCA to Optimal Control

Master PCA, ICA, RL, LQR & SVD to unlock dimensionality reduction, optimal control and AI mastery.

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30
Hours
LUNARTECH
August 2025
Available online
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Master Advanced Machine Learning: From PCA to Optimal Control

In this course,
you'll learn

Master Unsupervised Learning

Confidently apply PCA and SVD to reduce data dimensionality, unveiling patterns and boosting model performance.

Build Source Separation Pipelines

Use ICA to solve the cocktail party problem, separating audio sources for real-world signal processing tasks.

Optimize Decisions with RL

Implement Value and Policy Iteration on MDPs, tackling continuous states with fitted value methods.

Design Optimal Control Systems

Leverage LQR, DDP, and Kalman filters to craft controllers for complex, nonlinear and partially observable systems.

Your instructor

Vahe Aslanyan

Vahe Aslanyan has empowered hundreds of developers by clarifying complex AI and data science concepts, helping them innovate cutting-edge software solutions.

Trusted by over 10.000 students

Course program

Here's a glimpse of what you'll learn throughout the course

Module 1

Module 1: PCA Fundamentals

Master PCA intuition: normalize data, compute covariance matrix, and extract top eigenvectors to reduce dimensions.

Module 2

Module 2: PCA in Text & SVD

Apply LSI with PCA on text corpora: reduce noise, unveil latent semantics for document similarity, and leverage SVD efficiency.

Module 3

Module 3: ICA & Cocktail Party

Learn ICA for source separation: solve the cocktail party problem by assuming non-gaussian signals and deriving independent components.

Module 4

Module 4: Reinforcement Learning

Explore RL with MDPs: define states, actions, rewards, and transitions in grid worlds; grasp credit assignment challenges.

Module 5

Module 5: Value & Policy Iteration

Implement Value and Policy Iteration: apply Bellman equations to converge on optimal policies and understand computational tradeoffs.

Module 6

Module 6: Optimal Control & Beyond

Delve into LQR, DDP, Kalman filters and policy search: design optimal controllers for linear, nonlinear, and partially observable systems.

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