Lectures related to Belief propagation simplification

Covers classical and quantum computational complexity concepts and implications.

Delves into challenges of learning from probabilistic models, covering computational complexity, data reconstruction, and statistical gaps.

Complexity & Induction: Algorithms & Proofs

Covers worst-case complexity, algorithms, and proofs including mathematical induction and recursion.

Least Mean Squares with Features

Covers the concept of Least Mean Squares (LMs) with features and iterative updates.

Optimal Power Flow Solvers: Next Generation Advancements

Explores advancements in optimal power flow solvers, focusing on multiperiod and security-constrained optimization.

Complexity of Algorithms

Explores algorithm complexity, analyzing efficiency and worst-case scenarios of sorting algorithms.

Numerical Methods: Runge-Kutta Approximation

Covers the Runge-Kutta method for approximating solutions of differential equations.

Projection Pursuit Regression: Nonlinear Modeling and Interpretability

Explores Projection Pursuit Regression for nonlinear modeling and the trade-offs with interpretability in neural networks.

Energy System Modelling: Overview and Optimization Problems

Covers energy system modelling, optimization, scenarios, predictions, complexities, and controversies in energy models.

Linear Systems: Direct Methods

Explores linear systems, direct methods, Gauss elimination, LU decomposition, and computational complexity.

Elements of Computational Complexity

Introduces computational complexity, decision problems, quantum complexity, and probabilistic algorithms, including NP-hard and NP-complete problems.

State Space Models: Expressivity of Transformers

Covers state space models and the expressivity of transformers in sequence copying tasks.

The Wrong Method: Polynomial Interpolation

Explores the inefficiencies of the incorrect method for polynomial interpolation.

Subset Sum: LLL Algorithm

Covers the Subset Sum problem and the efficient LLL algorithm for finding solutions in lattice basis reduction.

Computational Complexity

Covers the basics of computational complexity, including big O notation and complexity classes.

Distributed Intelligent Systems: Division of Labor and Multi-Robot Coordination

Explores division of labor in natural systems, multi-robot coordination, and the challenges of uncertainty in market-based algorithms.

Naive Bayes: Gaussian Discriminant Analysis

Covers the Naive Bayes assumption, Gaussian Discriminant Analysis, ML estimates, and Kernel trick.

Generalization in Deep Learning

Explores generalization in deep learning, covering model complexity, implicit bias, and the double descent phenomenon.

Direct Methods for Linear Equations

Covers direct methods for solving linear equations and computation complexity of substitution.

Subquadratic Attention Mechanisms: State Space Models Overview

Covers subquadratic attention mechanisms and state space models, focusing on their theoretical foundations and practical implementations in machine learning.