Machine Learning for Decipherment Spring 2015

This course is about machine learning and natural language processing (NLP) methods applied to the task of decipherment. Codes, ciphers, and scripts are examples of things that need decipherment. The problem of decipherment is a canonical example of unsupervised learning, as there is no human annotation available. So, the course will focus on many unsupervised learning methods applicable to the decipherment task. We will also study how decipherment methods can be useful for other NLP tasks such as machine translation.

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Instructor

Anoop Sarkar

Office Hours: Mondays 10am-11am TASC1 9427

Discussion Board

on Coursys

Time and place

Tuesdays 2:30-4:20 @ SECB 1010, Thursdays 2:30-3:20 @ SECB 1010

before Feb 26, classes on Thursdays were held in AQ 5037

Calendar

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Textbook

No required textbook. Online readings provided in Syllabus.

Grading

Submit homework source code and check your grades on Coursys

• Two programming homeworks. (20%; 10% each)
• In class decipherment exercises. (15%)
• Language in 10. (5%)
• In class presentations. (20%)
• Poster presentation. (10%)
• Final project. (30%)