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YouTube

Big Data Analytics

California Institute of Technology via YouTube

Syllabus

1.1 Caltech Welcome - S.G. Djorgovski.
1.2 JPL Welcome - R. Doyle and D. Crichton.
2.1 Ashish Mahabal: Best Programming Practices (Part 1).
2.2 Ashish Mahabal: Best Programming Practices (Part 2).
2.3 Ashish Mahabal : Best Programming Practices (Part 3).
2.4 Ashish Mahabal : Best Programming Practices (Part 4).
3.1 Matthew Graham: Data (Part 1).
3.2 Matthew Graham: Data Models (Part 2).
3.3 Matthew Graham: Relational Databases (Part 3).
3.4 Matthew Graham: SQL 1 (Part 4).
3.5 Matthew Graham: Advanced SQL (Part 5).
3.6 Matthew Graham: Alternative database (Part 6).
4.1 Amy Braverman (Part 1): Inference and Uncertainty.
4.2 Amy Braverman (Part 2): Basic Probability - 1.
4.3 Amy Braverman (Part 3): Basic Probability - 2.
4.4 Amy Braverman (Part 4): Basics of Inference - 1.
4.5 Amy Braverman (Part 5): Basics of Inference - 2.
4.6 Amy Braverman (Part 6): The Bootstrap.
4.7 Amy Braverman (Part 7): Subsampling.
5.1 Ashish Mahabal : R (Part 1).
5.2 Ashish Mahabal : R (Part 2).
5.3 Ashish Mahabal : R (Part 3).
5.4 Ashish Mahabal : R (Part 4).
5.5 Ashish Mahabal : R (Part 5).
5.6 Ashish Mahabal : R (Part 6).
5.7 Ashish Mahabal : R (Part 7).
6.1 Ciro Donalek: Introduction to Machine Learning: General Aspects.
6.2 Ciro Donalek: Introduction to Machine Learning: Supervised Learning.
6.3 Ciro Donalek: Introduction to Machine Learning: Unsupervised Learning.
6.4 Ciro Donalek: Classification: general aspects.
6.5 Ciro Donalek: Classification: Neural Networks.
6.6 Ciro Donalek: Clustering: General Aspects.
6.7 Ciro Donalek: Clustering: k-Means.
6.8 Ciro Donalek: Clustering: Self-Organizing Maps.
7.1 Thomas Fuchs: Lecture 1: Decision Trees.
7.2 Thomas Fuchs: Lecture 2: Random Forests.
7.3 Thomas Fuchs: Lecture 3: Properties of Random Forests.
7.4 Thomas Fuchs: Lecture 4: Random Forests in Space Exploration.
7.5 Thomas Fuchs: Lecture 5: Random Forests in Cancer Research.
8.1 David Thompson (Part 1): Local Methods for Pattern Recognition.
8.2 David Thompson (Part 2): Nearest Neighbors and the Curse of Dimensionality.
8.3 David Thompson (Part 3): Feature Selection.
8.4 David Thompson (Part 4): Linear Dimensionality Reduction.
8.5 David Thompson (Part 5): Metric Learning.
8.6 David Thompson (Part 6): Nonlinear Dimensionality Reduction: KPCA.
9.1 Santiago Lombeyda: Lecture 1: What is Visualization?.
9.2 Santiago Lombeyda: Lecture 2: Understanding the Landscape.
9.3 Santiago Lombeyda: Lecture 3: A Tool Taxonomy.
9.4 Santiago Lombeyda: Lecture 4: Principles of Data Representation.
9.5 Santiago Lombeyda: Lecture 4a: ... on Color.
9.6 Santiago Lombeyda: Lecture 4b: ... on Mapping Multiple Dimensions.
9.7 Santiago Lombeyda: Lecture 5: Addressing Bottlenecks.
9.8 Santiago Lombeyda: Lecture 6: Putting It All Together.
10.1 Scott Davidoff (Part 1): Brief Introduction to Data Visualization.
10.2 Scott Davidoff (Part 2): Perception and Dimensional Mapping.
10.3 Scott Davidoff (Part 3): Visual Communication Fundamentals.
10.4 Scott Davidoff (Part 4): Multi-dimensional Mapping.
10.5 Scott Davidoff (Part 5): Graphs and Trees.
10.6 Scott Davidoff (Part 6): Interaction.
11.1 Introduction to Cloud Computing - J. Bunn.
11.2 Algorithmic Approaches to Big Data - M. Graham.
11.3 Matthew Graham: Semantics (Part 1).
11.4 Matthew Graham: Semantics (Part 2).
11.5 Practical Genetic Algorithms - J. Bunn.
12.1 Chris Mattmann (Part 1): Big Data Architecture: Fundamentals.
12.2 Chris Mattmann (Part 2): Big Data Architecture: Fundamentals.
12.3 Chris Mattmann (Part 3): Big Data Architecture: Fundamentals.
12.4 Chris Mattmann (Part 4): Content Detection and Analysis for Big Data.
12.5 Chris Mattmann (Part 5): Content Detection and Analysis for Big Data.
12.6 Chris Mattmann (Part 6): Content Detection and Analysis for Big Data.

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caltech

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