Mathematics and Machine Learning Program - September 3 to November 1, 2024

Mathematics and Machine Learning Program - September 3 to November 1, 2024

Harvard CMSA via YouTube Direct link

Stephane Mallat | Image Generation by Score Diffusion and the Renormalisation Group

8 of 65

8 of 65

Stephane Mallat | Image Generation by Score Diffusion and the Renormalisation Group

Class Central Classrooms beta

YouTube videos curated by Class Central.

Classroom Contents

Mathematics and Machine Learning Program - September 3 to November 1, 2024

Automatically move to the next video in the Classroom when playback concludes

  1. 1 Giorgi Butbaia | Machine learning smooth 4-genus of a knot
  2. 2 Petros Koumoutsakos| Learning the effective dynamics of complex systems
  3. 3 Tristan Buckmaster | Singularities in fluids
  4. 4 James Halverson | Learning the Topological Invariance of Knots
  5. 5 Kyu-Hwan Lee | Discovering New Mathematical Structures with Machine Learning
  6. 6 Eric Vanden Eijnden|Generative modeling w/flows & diffusions, w/applications to scientific computing
  7. 7 Bin Dong | AI for Mathematics: From Digitization to Intelligentization
  8. 8 Stephane Mallat | Image Generation by Score Diffusion and the Renormalisation Group
  9. 9 Wagner et al. | Sparse subgraphs of the d-cube with diameter d
  10. 10 Angelica Babei | Learning Euler factors of elliptic curves with transformers
  11. 11 Yang Hui He | AI assisted mathematics
  12. 12 Edgar Costa | Machine learning L-functions
  13. 13 Matej Balog | FunSearch: Mathematical discoveries from program search with large language models
  14. 14 Cengiz Pehlevan | Solvable Models of Scaling and Emergence in Deep Learning
  15. 15 Fabian Ruehle | Rigorous results from ML using RL
  16. 16 Ankit Anand and Abbas Mehrabian | From Theorem Proving to Disproving
  17. 17 Jürgen Jost | Data visualization with category theory and geometry
  18. 18 Deep Learning 10/22/24
  19. 19 Math and Machine Learning Program 10/21/24
  20. 20 Math and Machine Learning Program 10/23/24
  21. 21 Math and Machine Learning Program 10/17/24
  22. 22 Math and Machine Learning Program 10/15/24 | Tutorial on the Lean theorem prover
  23. 23 Math and Machine Learning Program Discussion 10/16/24
  24. 24 Deep Learning 10/15/24
  25. 25 Deep Learning 10/11/24
  26. 26 Deep Learning 9/19/24
  27. 27 Math and Machine Learning Program 10/7/24
  28. 28 Deep Learning 10/8/24
  29. 29 Math and Machine Learning Program 10/8/24
  30. 30 Math and Machine Learning Program 10/3/2024
  31. 31 Math and Machine Learning Program 10/2/2024
  32. 32 Math and Machine Learning Program | Panel discussion on Machine Learning in Science Education
  33. 33 Math and Machine Learning Program 9/30/2024
  34. 34 Deep Learning 10/1/2024
  35. 35 Math and Machine Learning Program 9/27/2024
  36. 36 Deep Learning 9/26/24
  37. 37 Deep Learning 9/24/2024
  38. 38 CMSA Math and Machine Learning Program 9/23/2024
  39. 39 Deep Learning 9/17/2024
  40. 40 Deep Learning 9/12/2024
  41. 41 Deep Learning 9/10/2024
  42. 42 Kun-Hsing Yu | Foundation Models for Real-Time Cancer Diagnosis
  43. 43 Boris Hanin | Scaling Limits of Neural Networks
  44. 44 Melanie Weber | Data and Model Geometry in Deep Learning
  45. 45 Bianca Dumitrascu|Statistical machine learning for learning representations of embryonic development
  46. 46 Tianxi Cai|Crowdsourcing with Multi-institutional EHR to Improve Reliability of Real World Evidence
  47. 47 Neil Thompson | How Algorithmic Progress is driving progress in Big Data and AI
  48. 48 Raj Chetty | The Science of Economic Opportunity: New Insights from Big Data
  49. 49 Kavita Ramanan | Understanding High-dimensional Stochastic Dynamics on Realistic Networks
  50. 50 Jamie Morgenstern | What governs predictive disparity in modern machine learning applications?
  51. 51 Peter Hull | Measuring Discrimination in Multi-Phase Systems with an Application to Child Protection
  52. 52 CMSA Math and Machine Learning Program 9/13/24
  53. 53 CMSA Math and Machine Learning Program 9/11/24
  54. 54 CMSA Math and Machine Learning Program 9/10/24
  55. 55 CMSA Math and Machine Learning Program 9/9/24
  56. 56 Geordie Williamson | Can AI help with hard mathematics?
  57. 57 Geordie Williamson | Using saliency analysis to discover structure
  58. 58 Mathematics and Machine Learning Program Opening Workshop | Panel Discussion
  59. 59 Leon Bottou | Conceptual challenges in modern AI
  60. 60 François Charton | Transformers meet Lyapunov
  61. 61 Adam Wagner | Case studies I: Reinforcement learning
  62. 62 Boris Hanin: Theory of Machine Learning
  63. 63 Panel Discussion: Automated mathematical discovery
  64. 64 David McAllester | Logic and formal methods
  65. 65 Mike Douglas | Overview of AI for mathematics

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.