Lectures related to Machine Learning in Chemistry: Bayesian Reaction Optimization

Understanding Generalization: Implicit Bias & Optimization

Explores the trade-off between complexity and risk in machine learning models, the benefits of overparametrization, and the implicit bias of optimization algorithms.

Deep Generative Models in Drug Discovery

Explores the application of deep generative models in drug discovery, focusing on designing small molecules and optimizing molecular structures.

Distributed Intelligent Systems: General Logistics and Course Overview

Covers the general logistics, course rationale, prerequisites, organization, credits, workload, grading, and course content, including swarm intelligence, foraging strategies, and collective phenomena.

Optimization Techniques: Convexity and Algorithms in Machine Learning

Covers optimization techniques in machine learning, focusing on convexity, algorithms, and their applications in ensuring efficient convergence to global minima.

Faster Gradient Descent: Projected Optimization Techniques

Covers faster gradient descent methods and projected gradient descent for constrained optimization in machine learning.

Chemical Reaction Optimization: Multi-Task Learning

Explores multi-task learning for accelerated chemical reaction optimization, showcasing challenges, automated workflows, and optimization algorithms.

Optimisation Strategies: Energy Systems Modelling and Optimization

Explores solving strategies for energy system optimization problems and different types of optimization approaches.

Motor control systems

Explores motor control systems, covering algorithms, sensor integration, and practical applications in robotics and automation.

Optimization Techniques: Convexity in Machine Learning

Covers optimization techniques in machine learning, focusing on convexity and its implications for efficient problem-solving.

Decentralized Optimization

Explores decentralized optimization in machine learning, emphasizing robustness, privacy, and fairness in collaborative learning.

Optimization Methods: Theory Discussion

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Multi-arm Bandits

Discusses algorithms for balancing exploration and exploitation in decision-making processes.

Optimization Techniques: Stochastic Gradient Descent and Beyond

Discusses optimization techniques in machine learning, focusing on stochastic gradient descent and its applications in constrained and non-convex problems.

Optimization with Constraints: KKT Conditions

Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.

MILP Model and Typical Days by FM

Discusses MILP model, typical days, clustering, and extreme periods analysis in energy systems optimization.

Feedback Equilibrium Seeking: Robust Control for Cyber-Physical Systems

Explores Feedback Equilibrium Seeking for robust control in cyber-physical systems, emphasizing reliability, scalability, and performance.

Proximal Gradient Descent: Optimization Techniques in Machine Learning

Discusses proximal gradient descent and its applications in optimizing machine learning algorithms.

Distributed Intelligent Systems: Self-Organization and Foraging Strategies

Explores self-organization in natural systems and foraging strategies of ants, including the Traveling Salesman Problem and Ant Colony Optimization algorithms.

Automating Scanning Probe Microscopy: Techniques and Applications

Covers the integration of machine learning with scanning probe microscopy for enhanced automation and efficiency in scientific workflows.

Implicit Bias in Machine Learning

Explores implicit bias, gradient descent, stability in optimization algorithms, and generalization bounds in machine learning.