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8.1 David Thompson (Part 1): Local Methods for Pattern Recognition

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8.1 David Thompson (Part 1): Local Methods for Pattern Recognition

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Big Data Analytics

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  1. 1 1.1 Caltech Welcome - S.G. Djorgovski
  2. 2 1.2 JPL Welcome - R. Doyle and D. Crichton
  3. 3 2.1 Ashish Mahabal: Best Programming Practices (Part 1)
  4. 4 2.2 Ashish Mahabal: Best Programming Practices (Part 2)
  5. 5 2.3 Ashish Mahabal : Best Programming Practices (Part 3)
  6. 6 2.4 Ashish Mahabal : Best Programming Practices (Part 4)
  7. 7 3.1 Matthew Graham: Data (Part 1)
  8. 8 3.2 Matthew Graham: Data Models (Part 2)
  9. 9 3.3 Matthew Graham: Relational Databases (Part 3)
  10. 10 3.4 Matthew Graham: SQL 1 (Part 4)
  11. 11 3.5 Matthew Graham: Advanced SQL (Part 5)
  12. 12 3.6 Matthew Graham: Alternative database (Part 6)
  13. 13 4.1 Amy Braverman (Part 1): Inference and Uncertainty
  14. 14 4.2 Amy Braverman (Part 2): Basic Probability - 1
  15. 15 4.3 Amy Braverman (Part 3): Basic Probability - 2
  16. 16 4.4 Amy Braverman (Part 4): Basics of Inference - 1
  17. 17 4.5 Amy Braverman (Part 5): Basics of Inference - 2
  18. 18 4.6 Amy Braverman (Part 6): The Bootstrap
  19. 19 4.7 Amy Braverman (Part 7): Subsampling
  20. 20 5.1 Ashish Mahabal : R (Part 1)
  21. 21 5.2 Ashish Mahabal : R (Part 2)
  22. 22 5.3 Ashish Mahabal : R (Part 3)
  23. 23 5.4 Ashish Mahabal : R (Part 4)
  24. 24 5.5 Ashish Mahabal : R (Part 5)
  25. 25 5.6 Ashish Mahabal : R (Part 6)
  26. 26 5.7 Ashish Mahabal : R (Part 7)
  27. 27 6.1 Ciro Donalek: Introduction to Machine Learning: General Aspects
  28. 28 6.2 Ciro Donalek: Introduction to Machine Learning: Supervised Learning
  29. 29 6.3 Ciro Donalek: Introduction to Machine Learning: Unsupervised Learning
  30. 30 6.4 Ciro Donalek: Classification: general aspects
  31. 31 6.5 Ciro Donalek: Classification: Neural Networks
  32. 32 6.6 Ciro Donalek: Clustering: General Aspects
  33. 33 6.7 Ciro Donalek: Clustering: k-Means
  34. 34 6.8 Ciro Donalek: Clustering: Self-Organizing Maps
  35. 35 7.1 Thomas Fuchs: Lecture 1: Decision Trees
  36. 36 7.2 Thomas Fuchs: Lecture 2: Random Forests
  37. 37 7.3 Thomas Fuchs: Lecture 3: Properties of Random Forests
  38. 38 7.4 Thomas Fuchs: Lecture 4: Random Forests in Space Exploration
  39. 39 7.5 Thomas Fuchs: Lecture 5: Random Forests in Cancer Research
  40. 40 8.1 David Thompson (Part 1): Local Methods for Pattern Recognition
  41. 41 8.2 David Thompson (Part 2): Nearest Neighbors and the Curse of Dimensionality
  42. 42 8.3 David Thompson (Part 3): Feature Selection
  43. 43 8.4 David Thompson (Part 4): Linear Dimensionality Reduction
  44. 44 8.5 David Thompson (Part 5): Metric Learning
  45. 45 8.6 David Thompson (Part 6): Nonlinear Dimensionality Reduction: KPCA
  46. 46 9.1 Santiago Lombeyda: Lecture 1: What is Visualization?
  47. 47 9.2 Santiago Lombeyda: Lecture 2: Understanding the Landscape
  48. 48 9.3 Santiago Lombeyda: Lecture 3: A Tool Taxonomy
  49. 49 9.4 Santiago Lombeyda: Lecture 4: Principles of Data Representation
  50. 50 9.5 Santiago Lombeyda: Lecture 4a: ... on Color
  51. 51 9.6 Santiago Lombeyda: Lecture 4b: ... on Mapping Multiple Dimensions
  52. 52 9.7 Santiago Lombeyda: Lecture 5: Addressing Bottlenecks
  53. 53 9.8 Santiago Lombeyda: Lecture 6: Putting It All Together
  54. 54 10.1 Scott Davidoff (Part 1): Brief Introduction to Data Visualization
  55. 55 10.2 Scott Davidoff (Part 2): Perception and Dimensional Mapping
  56. 56 10.3 Scott Davidoff (Part 3): Visual Communication Fundamentals
  57. 57 10.4 Scott Davidoff (Part 4): Multi-dimensional Mapping
  58. 58 10.5 Scott Davidoff (Part 5): Graphs and Trees
  59. 59 10.6 Scott Davidoff (Part 6): Interaction
  60. 60 11.1 Introduction to Cloud Computing - J. Bunn
  61. 61 11.2 Algorithmic Approaches to Big Data - M. Graham
  62. 62 11.3 Matthew Graham: Semantics (Part 1)
  63. 63 11.4 Matthew Graham: Semantics (Part 2)
  64. 64 11.5 Practical Genetic Algorithms - J. Bunn
  65. 65 12.1 Chris Mattmann (Part 1): Big Data Architecture: Fundamentals
  66. 66 12.2 Chris Mattmann (Part 2): Big Data Architecture: Fundamentals
  67. 67 12.3 Chris Mattmann (Part 3): Big Data Architecture: Fundamentals
  68. 68 12.4 Chris Mattmann (Part 4): Content Detection and Analysis for Big Data
  69. 69 12.5 Chris Mattmann (Part 5): Content Detection and Analysis for Big Data
  70. 70 12.6 Chris Mattmann (Part 6): Content Detection and Analysis for Big Data

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