Topics:

• Foundations of machine learning
• Loss functions
• linear regression
• logistic regression
• gradient-based optimization
• neural networks and backpropagation
• Deep learning tools
• Numpy
• Theano
• Keras
• Tensorflow
• Applications
• Relation extraction
• Knowledge graph embeddings
  
• Practical projects (NLP related, to be agreed on during the course)

Presentations:

• Relation Pattern Correspondences with CNNs

• Daria Palchik, Elena Neuburg, Marina Sedinkina

• Presenation: 1.2.

• Relation Pattern Correspondences with LSTMS; Relation Classification with Recursive CNNs

• Dan Lüdtke, Diego Martin Arroyo, Karl-Heinz Krachenfels

• Presentation: 1.2.

• Word2Vec Comparison: Binary Hierarchical Softmax

• Nicolai Ruhnau, Stefan Schweter

• Presentation: 1.2.

• Word2Vec Comparison: Two-Level Hierarchical Softmax

• Eduard Saller, Kevin Falkner

• Presentation: 8.2

• Relation Prediction without Tagged Candidates
  

• Costanza Conforti, Nina Pörner, Raphael Höps

• Presentation: 8.2.

• Irony and Sarcasm Prediction with LSTM and CNN

• Isabell Krammer, Jennifer Ling, Fedor Uvarov

• Presentation: 8.2.

• Language Modeling for Armenian

• Christian Meyer, Tsovinar Sekhleyan

• Presentation: 8.2

Course Material:

date	slides	homework	materials
Oct. 19, 2016	Machine learning overview I (pdf)	(pdf)
Oct. 26, 2016	Machine learning overview IIa  Introduction to Numpy Ia	(pdf)
Nov. 2, 2016	Machine learning overview II (pdf)  Introduction to Numpy I (pdf)	(pdf)  (gitlab)  (ipynb)
Nov. 9, 2016	Machine learning overview III (pdf)  Introduction to Theano Ia	(ipynb)
Nov. 15, 2016	Introduction to Theano I / CNNs (pdf)	(pdf)	MNIST example code (github)
Nov. 23, 2016	Introduction to Theano II / RNNs (pdf)	(ipynb)  (data)
Nov. 30, 2016	Introduction to Keras / Hyper-parameters (pdf)		Hyper-parameter optimization example script:  hyperopt.py
Dec. 7, 2016	Knowledge Base Population (pdf)
Dec. 14, 2016	Word2Vec (pdf)  Proposed project topics (pdf)		Mikolov et al.: Distributed representations of words and phrases and their compositionality. (pdf)  Mikolov et al.: Efficient Estimation of Word Representations in Vector Space. (pdf)  Goldberg & Levy: Word2Vec Explained. (pdf)
Dec. 21, 2016	Project Topics Q&A		Intro to Tensorflow (Stanford, pdf)
Jan. 11, 2017	Tensorflow
Jan. 18, 2017	Neural Attention
Jan. 25, 2017	Neural Machine Translation
Feb. 1, 2017	Project Presentations
Feb. 8, 2017	Project Presentations

More materials:
Bengio: Practical recommendations ... (arXiv)
Socher: Neural Tips and Tricks (pdf)