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