Prerequisites: Strong foundation in calculus, linear algebra, probability, and machine learning fundamentals (including basic gradient descent). Familiarity with Python and ML libraries (e.g., NumPy, TensorFlow/PyTorch) is required.

Level: Advanced

What You'll Learn

• Second-Order Methods

  Understand and implement Newton's method, Quasi-Newton methods (BFGS, L-BFGS), and trust-region methods.

• Adaptive Learning Rates

  Analyze and apply algorithms like AdaGrad, RMSprop, Adam, and their variants for efficient convergence.

• Large-Scale Optimization

  Implement variance reduction techniques (SAG, SVRG) and strategies for handling massive datasets.

• Distributed Optimization

  Understand parameter server architectures, synchronous/asynchronous updates, and communication-efficient algorithms.

• Non-Convex Optimization

  Analyze challenges in deep learning optimization and apply techniques to navigate complex loss landscapes.

• Convergence Analysis

  Evaluate the theoretical convergence properties and practical performance of different optimization algorithms.

• Implementation

  Gain hands-on experience implementing and tuning advanced optimization algorithms using common ML frameworks.