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