Introduction to Machine Learning Optimization Techniques
Intro to Machine Learning Optimization Techniques
Course Description
Course Description
This webinar series introduces the fundamental concepts and practical intuition behind optimization in machine learning, focusing on how models learn from data and improve their predictions. The course is designed for trainees from applied domains, particularly healthcare and data-driven fields, who seek to understand optimization techniques without requiring a deep mathematical background.
The course begins with foundational concepts, including machine learning models, loss functions, and gradient descent, providing an intuitive understanding of how models minimize error. It then explores efficient optimization strategies such as batch, stochastic, and mini-batch gradient descent, along with techniques to address common challenges like slow convergence and instability. Finally, the course introduces adaptive optimization methods, including AdaGrad, RMSProp, and Adam, highlighting their practical advantages and real-world applicability.
Throughout the course, real-world clinical scenarios are used to connect optimization techniques to decision-making processes, enabling trainees to interpret model behavior, evaluate performance, and make informed choices about optimization strategies. By the end of the course, trainees will have a clear conceptual understanding of how optimization drives machine learning and how to apply these ideas in practical settings.
This course equips trainees with the foundational knowledge required to understand, evaluate, and apply optimization techniques in modern machine learning workflows. It provides trainees with a strong conceptual foundation in machine learning optimization, enabling them to critically evaluate models, collaborate effectively with data scientists, and apply optimization techniques in real-world applications.
Audience
This training is designed for healthcare professionals, researchers, and trainees with an interest in data-driven decision-making, including clinicians, public health practitioners, and health data analysts. It is also suitable for individuals from related fields who collaborate with data scientists or work with clinical datasets. The expected experience level is beginner to intermediate.
Trainees are not required to have a strong background in machine learning or advanced mathematics, as the course emphasizes conceptual understanding and practical intuition. Basic familiarity with data concepts (e.g., datasets, variables) and some exposure to Python or data analysis tools is beneficial but not required. No prior experience with machine learning optimization techniques is necessary.
Course Structure
Module 1: Foundations
This module introduces the core concepts of machine learning optimization through intuitive, real-world examples. Learners explore how machine learning models make predictions, how loss functions measure prediction error, and how gradient descent enables models to learn while avoiding common issues such as overfitting and underfitting.
Module 2: Efficient Optimization Techniques
This module examines optimization strategies designed to improve learning efficiency for large-scale datasets. Learners compare batch, stochastic, and mini-batch gradient descent, explore challenges such as slow convergence and the ravine problem, and learn how momentum accelerates optimization and improves stability.
Module 3: Adaptive Optimization Techniques
This module introduces modern optimization algorithms that automatically adjust learning rates during training. Learners compare AdaGrad, RMSProp, and Adam, understand their strengths and limitations, and learn why adaptive optimization methods have become the standard choice for training modern machine learning models.
Learning Outcomes
By the end of this webinar series, trainees will be able to:
- Explain how machine learning models learn by minimizing a loss function
- Describe the role of gradient descent and the effect of learning rate on model performance
- Distinguish between overfitting and underfitting and interpret their impact on model behavior
- Compare different gradient descent strategies (batch, stochastic, and mini-batch) and their trade-offs
- Identify common optimization challenges, such as slow convergence and instability
- Explain how techniques such as momentum improve optimization performance
- Describe adaptive optimization techniques (AdaGrad, RMSProp, Adam) and how they adjust learning rates
- Justify the use of Adam as a default optimizer in many machine learning applications
- Select appropriate optimization strategies based on dataset size and problem characteristics
- Interpret model training behavior (e.g., loss curves) to evaluate optimization effectiveness
Instrumental Persons
- Legand L. Burge, PhD - MPI, AIM-AHEAD Coordinating Center, DSTC
- Toufeeq Syed, PhD - MPI, AIM-AHEAD Coordinating Center, Leadership Core
- Alyssa Parham, Program Manager, DSTC
- Desta Haileselassie Hagos, PhD - Howard University, DSTC
Funding
The AIM-AHEAD program is funded by NIH, Agreement No. 1OT2OD032581. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.