Course Description
Intelligent systems and interfaces have become more of a reality than being part of a sci-fi movie. Natural Language Processing (NLP) is one of the integral part of such interfaces. Statistics and Machine Learning (ML) have been used extensively to solve several problems in NLP. This course aims to introduce basic concepts in NLP, and to discuss statistical and ML models to solve NLP tasks. The course will focus on very specific NLP tasks and corresponding models.
NLP Topics - Language Model, Sequence Tagging, PoS Tagging, NER, Vector Space Models, Distributional Semantics, Distributed Semantics/word embedding.
ML Topics - Markov Models, HMM, Log-Linear Models, MEMM, CRF, Neural Language Model, Neural Network, Representation Learning.
Class Location and Time Calendar
Location: Room 1201 (at Core 1, 2nd Floor)
- Wednesday 4 - 4:55 PM
- Thursday 3 - 3:55 PM
- Friday 2 - 2:55 PM
Course Instructor
Teaching Assistants
(names in alphabetical order)
- Rijil T R
- Saptarshi Pyne
- Shubhanshu Sharma
- Sudha S
- Sunil Sahu
Grading Policy
Syllabus and References
Prerequisites
Background reading material will be made available before the respective lecture, if required.
Course Discussions
Piazza Forum (Through Piazza website or Piazza App within Canvas)
- While creating a 'New Post', please select appropriate Piazza folder(s). eg. logistics, project, exam.
- If you find a post useful, please upvote it by selecting the 'good answer' button.