Course topics
Text preprocessing
Feature extraction
Text classification
Part-of-Speech Tagging, Parsing
Sequential models (HMM, CRF, RNN, LSTM, etc.)
Knowledge representation and relations (ontology, taxonomy, KG, KB, etc.)
Topic modeling
Entities, Entity linking, Entity Disambiguation
Language modeling
Word embeddings
Course tools
Colab/IPython Notebook, NLTK, sklearn, gensim, numpy, scipy, TF
Prerequisites
Basic knowledge in python.
Familiarity with mathematical notation and scientific formalization.
Familiarity with basic probability theory and Bayesian statistics.
Familiarity with basic concepts of information retrieval (precision and recall).
Lecturer
Dr. Julia Proskurnia
Software Engineer at Google
Dr. Julia Proskurnia is currently working in Apps Intelligence in Google Zurich with particular interest in text analysis, summarisation and autocompletion. Prior to Google she obtained her PhD degree in EPFL, Lausanne where she was working towards profiling, modelling and facilitation of online activism on social media. During her PhD she filed a patent and published multiple papers in the top tier conferences, such as WWW, CIKM, ICWSM, etc.
She currently holds two master degree, first, with specialisation of Distributed Systems, that she obtained from both UPC, Barcelona and KTH, Stockholm, and second, with specialisation of Applied System Analysis and Decision Making from NTUU KPI, Ukraine. She had received numerous scholarships during her studies, including, Anita Borg Scholarship, GHC grant, President scholarship of NTUU KPI, etc. Her current interests are Applied Machine Learning, NLP, Time series prediction, Text mining.
Fields of interests: Applied Machine Learning, NLP, Time series prediction, Text mining, Information Retrieval, Distributed Systems.
Contacts:
toluolll@gmail.com
www.juliapro.xyz
