Big Data Analytics

CSE545 - Spring 2020

THESE SLIDES ARE FROM A PREVIOUS VERSION OF THE COURSE.

current slides are here.
Slides are intended as an outline and visual aid for the lecture given in class. They are not a replacement for comprehensive note taking or for the readings.

• Topic 1: What is Big Data?
• Topic 2: Stream Processing Algorithms
• Topic 3: Hadoop: Distributed Architecture, HDFS, and MapReduce
• Topic 4: Spark: Resilient Distributed Datasets as Workflow System [ Poverty CSV Mean Example ]
• Topic 5: Distributed TensorFlow [ TF Linear Reg. Gradient Descent Example ]
• Topic 6: Similarity Search (3-12), with notes from 3-10
• Topic 7: Link Analysis
• Topic 8: Large Scale Hypothesis Testing
• Topic 9: Recommendation Systems
• Topic 10: Large-Scale Machine Learning

Slides from previous offerings of the class may be found here: Spring 2019, Fall 2017.
Warning: some content from these slides may be outdated.