Course Material
- Course Info (02.04.2019): Slides
- Distributional Semantics Basics (09.04.2019): Slides
- Linear Algebra I (16.04.2019): Slides, Homework Nr. 1
- Linear Algebra II (23.04.2019): Slides, Homework Nr. 2
Notebook on Homework Nr. 1, Notebook on Linear Transformations,
Notebook on Linear Algebra with numpy - Linear Algebra II (Part II) (30.04.2019): Slides, Notebook on SVD, LSI, Notebook on exercise 3 of Homework Nr. 2, Homework Nr. 3
- Building Distributional Models, Distributional Memory (07.05.2019): Slides (Building Distributional Models), Slides (Distributional Memory), Homework Nr. 4, Notebook on exercise 3 of Homework Nr. 3
- Practical Exercise Nr.1 (14.05.2019): Material for the practical exercice, Solutions to the exercise
- Machine Learning: Linear Regression (28.05.2019): Slides, Homework Nr. 5, Notebook on Linear Regression
- Machine Learning: Logistic Regression (04.06.2019): Slides, Homework Nr. 6, Notebook on Logistic Regression
- Machine Learning: Neural Nets (11.06.2019): Slides, Homework Nr. 7
- Machine Learning: Neural Nets – Backpropagation (18.06.2019): Slides – CS231n Winter2016 (Andrej Karpathy), Video on backpropagation (relevant till minute 33), Homework Nr. 8, Notebook on exercise 1 of homework Nr. 7
- Word2vec (25.06.19): Slides
- Practical Exercise Nr.2 (02.07.2019): Material for Practical Exercise Nr.2, Solutions to exercise Nr. 2
Reading Suggestions
- Jurafsky, Dan & James H Martin. 2017. Speech and language processing. an introduction to natural language processing, computational linguistics and speech recognition. Draft of 3rd edition. (Chapter on Vector Semantics) [PDF]
- Manning, C., Raghavan, P., & Schütze, H. (2010). Introduction to information retrieval. Natural Language Engineering, 16(1), 100-103. (Chapter 6, 14 and 18) [PDF]
- Sahlgren, M. (2006). The Word-Space Model: Using distributional analysis to represent syntagmatic and paradigmatic relations between words in high-dimensional vector spaces (Doctoral dissertation). [PDF]
- Turney, P. D., & Pantel, P. (2010). From frequency to meaning: Vector space models of semantics. Journal of artificial intelligence research, 37, 141-188. [PDF]