Lectures related to Generative Models: Advancements in Molecular Design

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.

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Deep and Convolutional Networks: Generalization and Optimization

Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.

Decentralized Optimization

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

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Machine Learning: Supervised and Unsupervised Learning Techniques

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Car-Parrinello molecular dynamics

Explores Car-Parrinello molecular dynamics, a unified approach combining molecular dynamics and density-functional theory for simulating various systems, with a focus on historical background, technical details, and challenges in atomistic simulations.

Drug Discovery: Computational Approaches

Explores the EXSCALATE4COV project, focusing on computational drug discovery for COVID-19 treatments and the collaboration between academia and industry.

Optimization Techniques: Convexity in Machine Learning

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

Non-Convex Optimization: Techniques and Applications

Covers non-convex optimization techniques and their applications in machine learning.

Deep and Robust Reinforcement Learning Techniques

Discusses advanced reinforcement learning techniques, focusing on deep and robust methods, including actor-critic frameworks and adversarial learning strategies.

Gradient Descent: Principles and Applications

Covers gradient descent, its principles, applications, and convergence rates in optimization for machine learning.

Computational Molecular Design: Mathematical Theory, High Performance Computing, In Vivo Experiments

Explores Computational Molecular Design, emphasizing Mathematical Theory, High Performance Computing, and In Vivo Experiments.

Multilayer Neural Networks: Deep Learning

Covers the fundamentals of multilayer neural networks and deep learning.

Deep Generative Models: Variational Autoencoders & GANs

Explores Variational Autoencoders and Generative Adversarial Networks for deep generative modeling.

Introduction to Reinforcement Learning: Key Concepts and Applications

Introduces reinforcement learning, covering its definitions, applications, and theoretical foundations, while outlining the course structure and objectives.

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.

Chemical Reaction Optimization: Multi-Task Learning

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

Gradient Descent

Covers the concept of gradient descent, a universal algorithm used to find the minimum of a function.