AI for Biology - MIT 6.S191 Lecture 10

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

Subjects

Computer Science