Big Data and Large Scale Machine Learning

Transcription

1 CSE740: Project Ideas 12 Sept 2016

2 CSE740 Projects Mandatory for students enrolled for 2 or 3 credits To be done in groups of 3 Milestones: 1 Send in an to instructors with team information and schedule a one on one meeting - By September 24 2 Meet instructors to discuss project - By October 1 3 Mid-term progress report - By October 30 4 Final demonstration and project report due - Finals Week

3 Project Ideas - 1 Graph Analysis Problem: Measuring statistical properties of large graphs. Understanding temporal evolution of the network properties for peer-to-peer networks and AS networks. Algorithm: Network centrality, Kronecker Product Graph Models, Erdosy Renyi Models, Tensor decomposition based models for dynamic analysis. Data: Network data available from SNAP library: Technology: PEGASUS, SNAP, Hadoop

4 Project Ideas - 2 Financial Data Analysis Problem: Studying network properties of bipartite graphs to identify changes in US financial market (e.g., 2008 economic meltdown) Algorithm: Bipartite graph properties, Tensor decomposition based models for dynamic analysis. Data: Holdings data made available from Technology: PEGASUS, MapReduce, SQL

5 Project Ideas - 3 Healthcare Data Analysis Problem: Understanding disease progression for Chronic Kidney Disease patients Algorithm: Clustering, time series analysis, predictive models Data: Electronic health record data (anonymized) made available by the UB School of Medicine Technology: SQL, Postgres, Map Reduce

6 Project Ideas - 4 Image Understanding Problem: Understanding species movement and studying wildlife population in Tropical forests. Algorithm: Image analysis, predictive modeling, zero-shot learning Data: Annotated images captured by camera traps in sites across the world. Made available (publicly) by the group Conservation International. Technology: MapReduce, Image Databases

7 Project Ideas - 5 Comparing Distributed Machine Learning Frameworks Problem: Comparing performance of ML frameworks for an inference task. Algorithm: Bayesian inference, Topic Modeling using Latent Dirichlet Allocation Data: Any publicly available text corpus Technology: Spark, MapReduce, GraphLab

8 Project Ideas - 6 Classification Algorithms for Chinese Character Recognition Problem: Building a distributed classifier for Chinese character recognition. Algorithm: Neural networks, Logistic Regression Data: Download.html Technology: Spark, MapReduce, GraphLab

9 Project Ideas - 7 Understanding Bias in Black Box ML Systems Algorithm Audit Problem: Identify the inherent bias in machine learning algorithms in areas such as search, recommender systems, etc. Data: To be collected as part of the project Technology: Python

10 Computing Infrastructure Each team will be allowed to use resources on Microsoft Azure or Google Cloud Platform Please list your requirements in your initial proposal You are free to use resources such as elastic map reduce, spark clusters, databases, and others