This course syllabus was generated automatically by a natural language processing (NLP) algorithm in Japanese and automatically translated into English by a second NLP algorithm. You can ask your smart speaker to read this syllabus aloud using a third NLP algorithm and a fourth to transcribe the speech back into text. NLP is revolutionizing the analysis, generation, and transformation of text in a host of applications, from document analysis to language translation to speech recognition.
This course will review the past, present, and future of NLP, from classical approaches based on statistical language models to modern approaches based on geometrical embeddings and deep learning. We will also explore a range of applications.
This is a “reading course,” meaning that students will read and present papers from the technical literature to the rest of the class in a lively debate format. Discussions will aim at identifying common themes and important trends in the field. Students will also obtain valuable hands on experience with through a group project.
- Location: 1075 Duncan Hall
- Time: Friday 2pm
- Instructor: Richard Baraniuk
2028 Duncan Hall
Office Hours: By appointment
- Prerequisites: Required: Linear algebra, introduction to probability and statistics, familiarity with a programming language such as Python, R, or MATLAB. Desired: Knowledge of machine learning, signal processing, optimization, and deep learning
- Course Website: Piazza Course Management Site (It is mandatory that you use this site; all official announcements will be made there)
- Course Signup: Please add your information here if you are planning to take, audit, or sit in the class