AI for Biology - MIT 6.S191 Lecture 10

Overview

Dive into the fascinating world of artificial intelligence tailored for biological applications in this exclusive MIT lecture. Led by Ava Amini, Principal Research Scientist at Microsoft, this session is part of the esteemed MIT 6.S191 series, focusing on the integration of AI in biology.

Perfect for enthusiasts looking to expand their understanding of how deep learning can be applied in biological contexts.

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

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Subjects

Computer Science