| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
| This course aims to provide students with a foundational understanding of AI methods, focusing on applications in toxicology and environmental health. Through hands-on experience with Python for data analysis and visualization, students will gain familiarity with high-level AI algorithms. By the end of the course, students will have a comprehensive grasp of core AI methods, be introduced to research topics in toxicology and environmental health, and develop critical skills to assess, design, and implement AI-driven research projects. They will also learn to evaluate the strengths and limitations of AI applications in chemical exposure, toxicokinetic, toxicity, risk assessment, and related health impacts. |
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| topic Discussion/Production |
| Exercises |
| Lecturing |
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| Oral Presentation |
| Assignment |
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| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
Day 1: Introduction to Artificial Intelligence in Environmental Toxicology
Day 2: Data Preprocessing and Exploration for Toxicology and Environmental Health
Day 3: Supervised Learning in Toxicology and Environmental Health
Day 4: Unsupervised Learning for Exposure and Toxicokinetics Studies
Day 5: Deep Learning and Neural Networks for Environmental Health |
| Week 2 |
Day 6: AI for Risk Assessment and Research Design in Environmental Health |
| Week 3 |
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| Week 4 |
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| Week 5 |
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| Week 6 |
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| Week 7 |
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| Week 8 |
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| Week 9 |
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| Week 10 |
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| Week 11 |
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| Week 12 |
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| Week 13 |
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| Week 14 |
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| Week 15 |
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| Week 16 |
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self-directed
learning |
無自主學習內容 |
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| Evaluation |
• In-Class Exercises: Daily hands-on tasks to reinforce lecture content
• Final Project: A mini-project requiring students to apply AI methods learned to a topic of choice in environmental toxicology |
| Textbook & other References |
| Aurélien Géron. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. 2nd Edition. 2019. O'Reilly Media, Inc. Available online (UCR campus/VPN) at https://github.com/ageron/handson-ml2 |
| Teaching Aids & Teacher's Website |
| Lab website: https://weichunc.mystrikingly.com/ UCR website: https://envisci.ucr.edu/faculty |
| Office Hours |
| After class |
| Sustainable Development Goals, SDGs(Link URL) |
| 09.Industry, Innovation and Infrastructure | include experience courses：N |
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