An introduction to foundational ideas in computational linguistics, such as: n-gram language models; noisy channel models; supervised and unsupervised learning; and distributional semantics. Focus on developing an understanding of the intuitions behind the ideas, including relevant algorithms and math, and implementing this understanding in hands-on programming applications. May be taken for graduate credit using the LING 297 course code.