Location: NSH 1109

Time: Every Thursday, 4:30pm - 6:20pm

Course 11-765: Active Learning seeks to sample the most instructive instances to obtain labels in order to optimize performance (e.g. minimize the loss function) of different learning algorithms, multiple state-of-the-art methods will be examined including uncertainty sampling, density sampling, diversity sampling, ensemble and multi-strategy methods. We will also discuss extensions such as active learning to rank, proactive learning, and de-novo hidden class discovery.

I teach two modules:-

1. Course 08-710: Search Engines, Webpage Optimization
2. Course 08-711: Data Mining, CRM
   

15-381: Artificial Intelligence: Spring 2003
20-760: Web-Based Information Architectures: Fall 2003 (GSIA mini II)
15-381: Artificial Intelligence: Fall 2001
20-760: Web-Based Information Architectures: Summer 2001
15-381: Artificial Intelligence: Spring 2001
15-381: Artificial Intelligence: Fall 2000
11-682: Language Technologies: Fall 2000
20-760: Web-Based Information Architectures: Summer 2000
15-381: Artificial Intelligence: Spring 2000