고졸인데 역노화연구모델만드는 ai리서처될라면 어떤책읽어야함

지피티가 이 책들 추천해줬는데 지금 2번책읽고있거든

추가할거나 수정할거나 필요없는거있으면말해줘

책좋은거공유좀


| Step | Book | Brief Reason |

|------|------|--------------|

| 1. Math Bedrock | “Linear Algebra Done Right” (Axler) | Clean, proof-first view of vectors → essential for DL. |

| | “Probability Theory: The Logic of Science” (Jaynes) | Intuitive Bayesian mindset for modeling biology. |

| 2. Core ML | “Pattern Recognition and Machine Learning” (Bishop) | Classic ML “bible” covering probabilistic models. |

| | “Deep Learning” (Goodfellow, Bengio & Courville) | Definitive reference for modern neural nets. |

| 3. Software Craft | “Python Machine Learning” (Raschka) | Hands-on pipelines with scikit-learn & PyTorch. |

| | “Designing Data-Intensive Applications” (Kleppmann) | Teaches how to handle large omics datasets reliably. |

| 4. Bio Basics | “Molecular Biology of the Cell” (Alberts et al.) | Gold-standard cell/aging pathways primer. |

| | “Bioinformatics: Sequence and Genome Analysis” (Mount) | Algorithms for DNA/RNA/protein data you’ll model. |

| 5. Aging Science | “The Biology of Aging” (Jones & Rose) | Mechanistic walkthrough of senescence processes. |

| | “Lifespan” (Sinclair) | Integrates NAD⁺/epigenetic clock concepts->targets for ML. |

| | “Ending Aging” (de Grey & Rae) | SENS framework—defines rejuvenation damage categories. |

| 6. AI × Life Sci | “Deep Learning for the Life Sciences” (Green, Brenner, et al.) | End-to-end examples: genomics, chemistry, phenotyping. |

| | “Machine Learning in Healthcare” (Rudin et al.) | Covers interpretability—crucial for clinical aging work. |

| 7. Multi-omics & Causality | “Statistical Analysis of Gene Expression Microarray Data” (Kerr & Churchill) | Foundations for tranomic modeling. |

| | “Causal Inference in Statistics: A Primer” (Pearl) | Enables intervention predictions for rejuvenation. |

| 8. Generative & Chem-AI | “Generative Deep Learning” (O’Reilly) | GANs & VAEs for synthetic biology/compound design. |

| | “Advances in Computational Drug Design” (Pagadala) | Bridges molecule generation to senolytic discovery. |

| 9. Systems & Scaling | “Probabilistic Graphical Models” (Koller & Friedman) | Captures multi-scale aging networks. |

| | “Hands-On Machine Learning on Google Cloud” (Lakshmanan) | Teaches distributed training on large-omics datasets. |

| 10. Research & Ethics | “Writing Science” (Schimel) | Communicate findings for grant/paper success. |

| | “Ethics of Artificial Intelligence” (Floridi & Cowls) | Guides responsible deployment in medicine. |