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Related lectures (32)

Heuristic Optimization Methods

Explores heuristic optimization methods to find the global optimum efficiently.

Computational Aspects of Optimization MOOC: Simulation Neurocience

Explores optimization in neuron modeling, addressing underconstrained parameters, fitness functions, and successful fitting of firing patterns.

Optimization Techniques: Local Search, VNS, Simulated Annealing

Explores optimization techniques like local search, VNS, and simulated annealing.

Tools for Constraining Neuron Models MOOC: Simulation Neurocience

Covers tools for constraining neuron models using eFEL and BluePyOpt.

Constraint Satisfaction: Formulation and Algorithms

Covers the formulation of constraint satisfaction problems and systematic algorithms for solving them efficiently.

Bio-Inspired Learning: Neural Networks, Genetic Algorithms

Explores bio-inspired learning with neural networks and genetic algorithms, covering structure, training, and practical applications.

Optimising Neuron Models: eFEL and BluePyOpt

Introduces eFEL and BluePyOpt for optimizing neuron models with experimental data, using evolutionary algorithms.

Optimisation in Energy Systems

Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.

Fluorescent Protein Stability Assessment

Covers protein stability assessment using RFP and GFP, flow cytometry, mutagenesis, calcium reporters, and CRISPR-Cas9 experiments.

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.

Optimization and Simulation: Simulated Annealing

Explores simulated annealing for optimization, emphasizing parameter tuning and diversification to escape local minima.

Optimization Methods in Machine Learning

Explores optimization methods in machine learning, emphasizing gradients, costs, and computational efforts for efficient model training.

Chemical Reaction Optimization: Multi-Task Learning

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

Markov Chains and Algorithm Applications

Covers the application of Markov chains and algorithms for function optimization and graph colorings.

In Silico Neuroscience: Ion Channels and Neuronal Models

Explores detailed modeling of ion channels and neuronal morphologies in in silico neuroscience, covering neuron classification, ion channel kinetics, and experimental observations.

Optimization and Simulation: Heuristics and Neighborhoods

Explores greedy heuristics, neighborhoods in optimization, and local search algorithms.

Motor control systems

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

Structures in Non-Convex Optimization

Covers non-convex optimization, deep learning training problems, stochastic gradient descent, adaptive methods, and neural network architectures.

Curie Weiss Model

Covers the Curie-Weiss model in Statistical Physics, including magnetization probability, free entropy, and the cavity method.

Model Optimisation with eFel and BluePyOpt

Covers the process of optimising neuron model parameters using eFel and BluePyOpt.

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