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Starts 7 June 2025 13:02

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AI for Biology - MIT 6.S191 Lecture 10

Explore how AI is optimized for biological applications with Microsoft's Principal Research Scientist Ava Amini in this MIT deep learning lecture.
Alexander Amini via YouTube

Alexander Amini

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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
  • 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