This is a graduate course that focuses on the introducing of machine learning techniques that are used in Computational Linguistics.
Machine learning problems in CL are rather non-typical for machine learning because natural language includes a significant level of exceptions. The course will provide an overview of the most important machine learning algorithms, but it will mostly focus on how to apply machine learning to CL problems such as co-reference resolution, morphological analysis, parsing, and word sense disambiguation. In addition to the numerous underlying tasks in ML for CL (and NLP) applications, we will discuss deep learning approaches. We will work with neural network models applied to traditional CL and NLP problems.
Among others, we will cover word vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, convolutional neural networks, etc.
The course is a series of lectures and hands-on programming exercises.
I do not require any textbook, I recommend the following: