Module #1 Introduction to Data-Driven Approaches Overview of data-driven methods in computational chemistry, importance, and applications
Module #2 Basics of Computational Chemistry Review of fundamental concepts in computational chemistry, including quantum mechanics and molecular dynamics
Module #3 Data Sources and Curation Introduction to datasets and databases in computational chemistry, data curation, and preprocessing
Module #4 Machine Learning Fundamentals Introduction to machine learning concepts, supervised and unsupervised learning, and regression analysis
Module #5 Featurization and Descriptor Selection Methods for featurizing molecular data, including molecular descriptors and fingerprints
Module #6 Supervised Learning in Computational Chemistry Applications of supervised learning in computational chemistry, including QSAR, QSAR modeling, and property prediction
Module #7 Unsupervised Learning in Computational Chemistry Applications of unsupervised learning in computational chemistry, including clustering, dimensionality reduction, and anomaly detection
Module #8 Deep Learning in Computational Chemistry Introduction to deep learning concepts, including neural networks and convolutional neural networks
Module #9 Generative Models in Computational Chemistry Applications of generative models, including generative adversarial networks (GANs) and variational autoencoders (VAEs)
Module #10 Bayesian Methods in Computational Chemistry Introduction to Bayesian methods, including Bayesian linear regression and Gaussian processes
Module #11 Data-Driven Approaches to Molecular Design Applications of data-driven approaches to molecular design, including de novo design and optimization
Module #12 Data-Driven Approaches to Reaction Prediction Applications of data-driven approaches to reaction prediction, including reaction classification and retrosynthesis
Module #13 Data-Driven Approaches to Materials Science Applications of data-driven approaches to materials science, including materials property prediction and materials discovery
Module #14 Uncertainty Quantification and Error Analysis Methods for quantifying uncertainty and error analysis in data-driven approaches
Module #15 High-Performance Computing and Parallelization Introduction to high-performance computing and parallelization in data-driven approaches
Module #16 Case Studies in Data-Driven Computational Chemistry Real-world case studies and applications of data-driven approaches in computational chemistry
Module #17 Best Practices and Future Directions Best practices for implementing data-driven approaches in computational chemistry and future directions
Module #18 Final Project and Presentations Student projects and presentations applying data-driven approaches to a computational chemistry problem
Module #19 Computational Tools and Software Introduction to popular computational tools and software for data-driven approaches in computational chemistry
Module #20 Data Visualization and Communication Methods for visualizing and communicating data-driven results in computational chemistry
Module #21 Ethics and Responsibility in Data-Driven Computational Chemistry Ethical considerations and responsibilities in developing and applying data-driven approaches
Module #22 Collaboration and Interdisciplinary Approaches Importance of collaboration and interdisciplinary approaches in data-driven computational chemistry
Module #23 Domain Knowledge and Expertise Role of domain knowledge and expertise in developing and applying data-driven approaches
Module #24 Data Quality and Integrity Importance of data quality and integrity in data-driven approaches
Module #25 Explainability and Transparency Methods for explainability and transparency in data-driven approaches
Module #26 Domain Adaptation and Transfer Learning Methods for domain adaptation and transfer learning in data-driven approaches
Module #27 Active Learning and Online Learning Methods for active learning and online learning in data-driven approaches
Module #28 Course Wrap-Up & Conclusion Planning next steps in Data-Driven Approaches in Computational Chemistry career
