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International Conference on Optimization Techniques with Machine Learning

ICOTML

18th Jun – 19th Jun 2027 Mexico City, Mexico

Official Invitation Letter Available

An official invitation letter will be provided upon successful registration for your participation in the conference.

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Access to All Conference Sessions

Plenary, keynote and parallel sessions

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Official invitation letter after successful registration

Conference Kit / Digital Materials

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Conference Session Tracks

UN SDG Wheel

Aligned with UN Sustainable Development Goals

The conference's session tracks effectively support the following SDGs.

SDG 4 SDG 7 SDG 9 SDG 12
01 Advancements in Gradient Descent Techniques +
This track focuses on the latest developments in gradient descent algorithms, emphasizing their application in machine learning optimization. Participants will explore novel approaches to enhance convergence rates and accuracy in various engineering contexts.
SDG 9 SDG 12
02 Convex Optimization in Engineering Applications +
This session delves into the role of convex optimization in solving complex engineering problems. Researchers will present innovative methods and case studies showcasing the effectiveness of convex approaches in machine learning.
SDG 9 SDG 12
03 Metaheuristic Algorithms for Optimization Challenges +
This track examines the application of metaheuristic algorithms in tackling optimization challenges across different engineering domains. Participants will discuss their effectiveness in finding near-optimal solutions for complex problems.
SDG 9 SDG 12
04 Reinforcement Learning for Resource Allocation +
This session highlights the use of reinforcement learning techniques for efficient resource allocation in engineering systems. Attendees will explore case studies and methodologies that demonstrate the potential of RL in optimizing resource management.
SDG 7 SDG 12
05 Predictive Modeling Techniques in Engineering +
This track focuses on advanced predictive modeling techniques utilizing machine learning for engineering applications. Participants will share insights on model development, validation, and deployment in real-world scenarios.
SDG 9 SDG 12
06 Feature Selection and Dimensionality Reduction +
This session addresses the critical aspects of feature selection and dimensionality reduction in machine learning. Researchers will present methodologies that enhance model performance while maintaining interpretability.
SDG 4 SDG 9
07 Supervised vs. Unsupervised Learning in Engineering +
This track explores the distinctions and applications of supervised and unsupervised learning techniques in engineering. Participants will discuss the implications of each approach on model accuracy and applicability.
SDG 4 SDG 9
08 Anomaly Detection Techniques in Engineering Systems +
This session focuses on innovative anomaly detection techniques tailored for engineering applications. Researchers will present methodologies that effectively identify and mitigate anomalies in complex datasets.
SDG 9 SDG 12
09 Deep Learning Architectures for Optimization +
This track examines the integration of deep learning architectures in optimization processes. Participants will explore how deep learning can enhance traditional optimization techniques across various engineering fields.
SDG 9 SDG 12
10 Evolutionary Algorithms in Complex Problem Solving +
This session highlights the application of evolutionary algorithms in solving complex optimization problems. Researchers will share their findings on the effectiveness and adaptability of these algorithms in engineering contexts.
SDG 9 SDG 12
11 Swarm Intelligence and Optimization Strategies +
This track investigates the role of swarm intelligence in developing optimization strategies for engineering applications. Participants will discuss various swarm-based algorithms and their effectiveness in solving real-world optimization challenges.
SDG 9 SDG 12