This course covers topics related to Artificial Intelligence, Machine Learning, Natural Language Processing, and Lisp programming. Students will learn how to interpret AI concepts, work with Common Lisp, understand Semantic Spaces, Classification, Clustering, Word Embedding Models, Language Models, Hidden Markov Model, and Context-Free Grammars. The teaching method includes screencasts of lectures. This course is intended for individuals interested in expanding their knowledge of AI, ML, NLP, and Lisp programming.
Overview
Syllabus
Introduction: How to Interpret AI, ML, NLP, and Lisp (Lecture 1; August 23, 2017)
Introduction to Common Lisp (Lecture 2; August 30, 2017)
Mastering Common Lisp (Lecture 3; September 6, 2017)
Semantic Spaces (Lecture 4; September 13, 2017)
Classification (Lecture 5; September 20, 2017)
Clustering (Lecture 6; September 27, 2017)
Word Embedding Models (Lecture 7; October 4, 2017)
Language Models (Lecture 8; October 11, 2017)
Hidden Markov Model (Lecture 9; October 18, 2017)
Context-Free Grammars and Parsing (Lecture 10; October 25, 2017)
November 10, 2016
Generalized Chart Parsing (Lecture 12; November 15, 2017)
Wrap-Up and Exam Preparation (Lecture 13; November 22, 2017)
Taught by
INF4820
