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Each of the pages linked below represents one day of the school, and contains the links to the lecture videos and other materials. The instructors names are given in parenthesis. Additional links will be added during the school's duration (Sep. 2-12, 2014). For a self-paced study, you do not have to follow these in this particular order, although there are some obvious logical sequences.

Click on the Day link, and then follow the links shown on that page. Check out also our general useful links.

Day 1:

• Introduction and a Broader Context (G. Djorgovski, D. Crichton, R. Doyle)
• Big Data Architecture: Fundamentals (C. Mattman)
• Introduction to Machine Learning (C. Donalek)

Day 2:

• Best programming practices (A. Mahabal)
• Content Detection and Analysis for Big Data (C. Mattmann)

Day 3:

• Introduction to R (A. Mahabal)

Day 4:

• Inference and Uncertainty (A. Braverman)

Day 5:

• Databases (M. Graham)

Day 6:

• Data Visualization (S. Davidoff)

Day 7:

• Clustering and Classification (C. Donalek)

Day 8:

• Decision Trees and Random Forests (T. Fuchs)

Day 9:

• Dimensionality Reduction (D. Thompson)

New materials:

• Semantic Web Technologies (M. Graham)
• More on Data Visualization (S. Lombeyda)
• Genetic Algorithms (J. Bunn)
• Introduction to Cloud Computing (J. Bunn, M. Graham)