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Starts 7 June 2025 13:02
Ends 7 June 2025
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57 minutes
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Overview
Explore how AI is optimized for biological applications with Microsoft's Principal Research Scientist Ava Amini in this MIT deep learning lecture.
Syllabus
- Introduction to AI in Biology
- Basics of Deep Learning in Biology
- Biological Data Acquisition and Preprocessing
- Case Studies in AI Applications
- Machine Learning Models for Biology
- AI Tools and Platforms
- Challenges and Opportunities
- Guest Lecture by Ava Amini
- Conclusion and Further Resources
Overview of AI applications in biological sciences
Importance of AI in solving biological challenges
Neural networks and biological data
Key algorithms and frameworks
Data types in biological research (genomics, proteomics, imaging)
Data collection techniques
Preparing biological data for AI models
Handling unstructured and high-dimensional data
Genomic sequence analysis
Protein structure prediction
Drug discovery and development
Supervised vs unsupervised learning in biological contexts
Model selection and evaluation metrics
Ethical considerations and biases in biological datasets
Overview of popular AI tools (Microsoft AI, TensorFlow, PyTorch)
Cloud-based AI solutions for biological data analysis
Integration with existing biological research tools
Computational limitations and potential solutions
Future trends and research directions in AI for biology
Insights from Microsoft’s Principal Research Scientist
Discussion on recent advancements and breakthroughs
Q&A session on practical implementations
Recap of key concepts covered
Suggested readings and research papers
Networking opportunities and academic collaborations in AI for biology
Subjects
Computer Science