Module #1 Introduction to Predictive Modeling Overview of predictive modeling, importance in marine biodiversity, and course objectives
Module #2 Types of Predictive Models Introduction to machine learning, statistical, and process-based models for marine biodiversity
Module #3 Challenges and Opportunities in Marine Biodiversity Modeling Discussing the complexities of marine ecosystems and the need for predictive modeling
Module #4 Data Sources for Marine Biodiversity Modeling Overview of data sources, including field observations, remote sensing, and citizen science
Module #5 Data Preprocessing and Cleaning Best practices for handling missing data, data normalization, and data transformation
Module #6 Feature Engineering for Marine Biodiversity Data Techniques for extracting relevant features from marine biodiversity data
Module #7 Supervised Learning for Species Distribution Modeling Introduction to supervised learning methods for predicting species presence/absence
Module #8 Unsupervised Learning for Community Analysis Clustering and dimensionality reduction techniques for marine biodiversity community analysis
Module #9 Random Forest for Marine Biodiversity Modeling Introduction to random forest, a popular machine learning algorithm for marine biodiversity modeling
Module #10 Deep Learning for Marine Biodiversity Introduction to deep learning, including convolutional neural networks and recurrent neural networks
Module #11 Model Evaluation and Selection Metrics and techniques for evaluating and selecting predictive models
Module #12 Handling Class Imbalance in Marine Biodiversity Data Strategies for dealing with class imbalance, a common problem in marine biodiversity modeling
Module #13 Generalized Linear Models for Marine Biodiversity Introduction to generalized linear models, including logistic regression and Poisson regression
Module #14 Generalized Additive Models for Marine Biodiversity Introduction to generalized additive models for non-linear relationships
Module #15 Bayesian Modeling for Marine Biodiversity Introduction to Bayesian inference and modeling for marine biodiversity
Module #16 Introduction to Process-Based Models Overview of process-based models, including mechanistic and individual-based models
Module #17 Ecological Models for Marine Biodiversity Introduction to ecological models, including predator-prey models and food web models
Module #18 Future Directions in Process-Based Modeling for Marine Biodiversity Discussion of future directions and applications of process-based models
Module #19 Predicting Species Distributions in Marine Ecosystems Case studies of predicting species distributions using machine learning and statistical models
Module #20 Monitoring Marine Biodiversity Using Remote Sensing Case studies of using remote sensing data for marine biodiversity monitoring
Module #21 Conservation Planning Using Predictive Modeling Case studies of using predictive modeling for conservation planning in marine ecosystems
Module #22 Marine Biodiversity and Climate Change Discussion of the role of predictive modeling in understanding and mitigating the impacts of climate change on marine biodiversity
Module #23 Uncertainty Quantification in Marine Biodiversity Modeling Discussion of uncertainty quantification methods and their importance in marine biodiversity modeling
Module #24 Integrating Machine Learning and Process-Based Models Discussion of hybrid approaches combining machine learning and process-based models
Module #25 Future Directions in Predictive Modeling for Marine Biodiversity Discussion of emerging trends and future directions in predictive modeling for marine biodiversity
Module #26 Final Project:Applied Predictive Modeling for Marine Biodiversity Students apply predictive modeling techniques to a real-world problem
Module #27 Final Project Presentations Students present their final projects and receive feedback
Module #28 Course Wrap-Up and Next Steps Course wrap-up, resources for further learning, and next steps for applying predictive modeling in marine biodiversity
Module #29 Additional Resources and Readings Supplementary readings and resources for further learning
Module #30 Course Wrap-Up & Conclusion Planning next steps in Predictive Modeling for Marine Biodiversity career
