The list of topics for students' talks

ML packages: Vowpal Wabbit

Resources:

• The Vowpal Wabbit webpage: http://hunch.net/~vw/
• The Vowpal Wabbit github repositoty: https://github.com/JohnLangford/vowpal_wabbit

ML packages: GraphLab

Resources:

• The GraphLab webpage: https://turi.com/

ML packages: MLib

Resources:

• The MLib webpage: http://spark.apache.org/mllib/

Deep networks: Theano

Resources:

• The Theano webpage: http://deeplearning.net/software/theano/

Deep networks: TensorFlow

Resources:

• The TensorFlow webpage: https://www.tensorflow.org/

Deep networks: Torch

Resources:

• The Torch webpage: http://torch.ch/

NLP packages: word2vec and FastText

Resources:

• One of the most important articles: https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf
• The Word2vec project: https://code.google.com/p/word2vec/
• The FastText project: https://github.com/facebookresearch/fastText

OpenAI Gym

Resources:

• The OpenAI Gym webpage: https://gym.openai.com/

XGBoost

Resources:

• The XGBoost webpage: http://xgboost.readthedocs.io/en/latest/

Active learning

Resources:

• A good introduction to the problem: http://burrsettles.com/pub/settles.activelearning.pdf

Transfer learning

Resources:

• A good starting point: https://machinelearningmastery.com/transfer-learning-for-deep-learning/

Positive-unlabelled learning

Resources:

• One of the first articles on this topic: https://www.cs.uic.edu/~liub/publications/ICDM-03.pdf
• Something more reasonable: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.140.9201&rep=rep1&type=pdf
• Something more recent: https://www.kdd.org/kdd2016/papers/files/rfp0261-jainA.pdf

Multi-label classification

Resources:

• A more theoretical introduction to this topic: https://link.springer.com/article/10.1007/s10994-012-5285-8
• Multi-label problems with extremely large label spaces: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/PrabhuVarmaKDD14.pdf
• And an algorithm from our university: https://arxiv.org/abs/1810.11671

Zero-shot learning

Resources:

• Theoretical introduction to the problem: https://arxiv.org/abs/1703.04394
• A tutorial from CVPR: https://www.youtube.com/watch?v=dE4nU5OaQqA

Structured-output prediction

Resources:

• Slides with the introdcution to the problem: http://icml.cc/2015/tutorials/AdvancesStructuredPrediction.pdf

Deep reinforcement learning

Resources:

• A good video lecture: http://videolectures.net/rldm2015_silver_reinforcement_learning/

Community detection

Resources:

• A good starting point: https://en.wikipedia.org/wiki/Community_structure
• A classifical paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC122977