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所在平台: Udemy |
课程主页: https://www.udemy.com/course/material-informatics/
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**课程名称：** 材料信息学：材料科学中的数据科学 **课程概述：** 本课程将人工智能、机器学习和数据科学与材料工程相结合，带您探索材料科学的未来。无论您是学生、研究人员还是职业人士，本课程都将帮助您掌握材料设计与信息学交叉领域的强大应用。 您将学习如何处理真实的材料数据集，应用决策树、聚类和人工神经网络等现代机器学习技术，并利用ChatGPT和Materials Project API加速材料的发现和设计。 **您将学到：** * 材料信息学的基本原理及其在材料设计中的作用 * 材料科学特有的统计和机器学习方法 * 材料领域的数据挖掘、数据预处理和数据库管理 * 材料科学数据库和API的实操 * 在材料开发中处理图像、图和符号数据 * 包括贝叶斯优化和超参数优化在内的优化技术 * 高级数据可视化和可解释的机器学习 * 高通量实验和结构预测导论 * Python、Jupyter Notebook和虚拟现实工具的应用 * 增材制造和结构材料的案例研究 **工具与技术：** * Python * Jupyter Notebook * Materials Project API * 机器学习算法 * 合成数据生成 **适合人群：** * 材料科学与工程专业的学生 * 进入材料设计领域的数据科学家 * 机械、冶金和化学工程师 * 纳米技术、冶金或增材制造领域的研究人员 * 任何对人工智能驱动的材料开发未来感兴趣的人
Material Informatics: AI, Machine Learning & Data Science in MaterialsUnlock the future of materials science with this comprehensive course on Material Informatics - where AI, Machine Learning, and Data Science meet materials engineering. Whether you're a student, researcher, or professional, this course will help you explore the powerful intersection of materials design and informatics.In this hands-on course, you'll learn how to work with real-world material datasets, apply modern ML techniques like decision trees, clustering, and ANN, and even use tools like ChatGPT and the Materials Project API to accelerate materials discovery and design. What You'll Learn:Fundamentals of materials informatics and its role in materials designStatistical and machine learning methods tailored for material scienceData mining, data preprocessing, and database management for materialsHands-on with materials science databases and APIsWorking with images, graphs, and symbolic data in material developmentOptimization techniques including Bayesian and hyperparameter optimizationAdvanced data visualization and interpretable MLIntroduction to high-throughput experiments and structure predictionUse of Python, Jupyter Notebook, and virtual reality toolsCase studies from Additive Manufacturing and structural materialsTools & Technologies:Python, Jupyter Notebook, Materials Project APIMachine Learning AlgorithmsSynthetic data generation Who Should Enroll:Materials Science & Engineering studentsData Scientists entering material designMechanical, Metallurgical & Chemical EngineersResearchers in nanotechnology, metallurgy, or additive manufacturingAnyone interested in the future of AI-driven material development
