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Topic · computational biology

computational biology

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  1. STATS M254 - Stat Methods in Comp Bio (Spring 2024) - Lecture 15: diff expression; multiple testingUCLA STATS M254 - Statistical Methods in Computational Biology (Spring 2024)Notes
  2. STATS M254 - Stat Methods in Comp Biology (Spring 2024) - Lecture 14: non-linear dimension reductionUCLA STATS M254 - Statistical Methods in Computational Biology (Spring 2024)Notes
  3. STATS M254 - Stat Methods in Comp Bio (Spring 2024) Lec 13: Louvain and Leiden clustering; ARI & AMIUCLA STATS M254 - Statistical Methods in Computational Biology (Spring 2024)Notes
  4. STATS M254 - Stat Methods in Comp Bio (Spring 2024) - Lec 12 (K-means, hclust, Louvain clustering)UCLA STATS M254 - Statistical Methods in Computational Biology (Spring 2024)Notes
  5. STATS M254 - Statistical Methods in Computational Biology (Spring 2024) - Lecture 11 (ICA; K-means)UCLA STATS M254 - Statistical Methods in Computational Biology (Spring 2024)Notes
  6. STATS M254 - Statistical Methods in Computational Biology (Spring 2024) - Lecture 10 (NMF, PNMF)UCLA STATS M254 - Statistical Methods in Computational Biology (Spring 2024)Notes
  7. STATS M254 - Statistical Methods in Computational Biology (Spring 2024) - Lecture 9 (GLM-PCA; MDS)UCLA STATS M254 - Statistical Methods in Computational Biology (Spring 2024)Notes
  8. STATS M254 - Statistical Methods in Computational Biology (Spring 2024) - Lecture 8 (# of PCs; GLM)UCLA STATS M254 - Statistical Methods in Computational Biology (Spring 2024)Notes
  9. STATS M254 - Statistical Methods in Computational Biology (Spring 2024) - Lecture 7 (PCA continued)UCLA STATS M254 - Statistical Methods in Computational Biology (Spring 2024)Notes
  10. STATS M254 - Stats Methods in Comp Bio (Spring 2024) - Lecture 6 (principal component analysis)UCLA STATS M254 - Statistical Methods in Computational Biology (Spring 2024)Notes
  11. Deep Learning -- Yoshua Bengio (Part 1)All TalksNotes
  12. Deep Learning -- Yoshua Bengio (Part 3)All TalksNotes
  13. Kernel methods and computational biology -- Jean-Philippe Vert (Part 1)All TalksNotes
  14. Introduction to Machine Learning -- Neil Lawrence (Part 1)All TalksNotes
  15. Probabilistic Modelling -- Iain Murray (Part 2)All TalksNotes
  16. Big Data and Large Scale Inference -- Amr Ahmed (Part 2)All TalksNotes
  17. Hamiltonian Monte Carlo and Stan -- Michael Betancourt (Part 2)All TalksNotes
  18. What is Machine Learning: A Probabilistic Perspective -- Neil Lawrence (Part 2)All TalksNotes
  19. Deep Learning -- Yoshua Bengio (Part 2)All TalksNotes
  20. Probabilistic Modelling -- Iain Murray (Part 3)All TalksNotes

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