Resources

Keynotes

• [ML] Machine Learning Workflow
• [RecSys] Introduction to Recommender Systems
• [ML] Introduction to Machine Learning
• [ML] Data Integration (Keynote) zip pdf
• [ML] Introduction to Exploratory Analysis: Measures, Visualizations and Practical Examples zip pdf
• [REC] Introduction to Multi-Armed Bandits
• [ML] Useful tips for working in Jupyter Notebooks
• [REC] Recommender Systems in Practice (Part I)
• [NLP] What about deep NLP?

Research papers

• [Rec] Joseph A. Konstan, John Riedl: Recommender systems: from algorithms to user experience
• [Rec] Qian Zhao, Shuo Chang, F. Maxwell Harper, and Joseph A. Konstan: Gaze Prediction for Recommender Systems (slides by Michal Kompan)
• [Rec] Yiming Liu, et al.: Are You Influenced by Others When Rating?: Improve Rating Prediction by Conformity Modeling (slides by Peter Gašpar)
• [Rec] Michael D. Ekstrand, et al.: Letting Users Choose Recommender Algorithms: An Experimental Study (slides by Peter Gašpar)
• [ML] Pedro Domingos: A Few Useful Things to Know about Machine Learning (slides by Ondrej Kaššák)
• [NLP] Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean: Efficient Estimation of Word Representations in Vector Space

Books

• [Rec] Recommender Systems – The Textbook
• [Rec] Recommender Systems Handbook (2nd Edition)
• [ML] Introduction to Statistical Machine Learning
• [ML] An Introduction to Statistical Learning with Applications in R

Books (available in printed form)

• Information Visualization: An Introduction
• Information Visualization: Perception for Design (Interactive Technologies)
• Bayesian Statistics the Fun Way
• The Craft of Scientific Presentations
• The Craft of Scientific Writing

Blogs

• [AI] Towards AI
• [Data Science] Towards Data Science
• [Data Science] Analytics Vidhya
• [Data Science] Data Science @ Berkeley
• [Data Science] A Comprehensive Guide to Data Exploration
• [Computer Science] Map of Computer Science (Static map to download)
• [Data Science] Machine learning mastery
• [Data Science] KDnuggets

Courses

• [ML] Machine learning at Coursera (blog)
• [ML] Python For Data Science at edX (blog)
• [NLP] Introduction to NLP at Coursera (blog)

How to Start with Python

• Python basics
• Practical introduction to Python
• Introduction to Pandas
• Practical examples in Pandas
• Introduction to Data Science
• Another complex tutorial for Data Science
• An Introduction to Statistical Learning
• Machine learning in Python
• Useful Python notebooks
• Another gallery of interesting Jupyter and IPython Notebooks
• More resources

Datasets

• Google Dataset Search
• Academic Torents
• Yelp
• [REC] MovieLens

Technologies

• Bayesian blocks: Histogram bin size selection

AI Research Residency Programs (source)

• Google AI Residency Program
• Facebook AI Residency Program
• Microsoft AI Residency Program
• Microsoft NERD Artificial Intelligence Program
• Uber AI Residency Program

Computation resources

• Data centre – 736 cores, 10TB ram, 100TB disk
• Smart – 16 nodes, 128 cores, 0.6TB ram, 48TB disk

Misc

• Datalys Presentation Template (last update: 19th September 2017)
• Datalys Slovak-English Domain Term Dictionary