Subareas

• Knowledge Representation
• Machine Learning
• Computer Vision
• Natural Language Processing
• Big Data, Data Science, Data Mining

Books

Theory

1. Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig
2. Artificial Intelligence by Elaine A. Rich and Kevin Knight
3. Artificial Intelligence by Patrick Winston
4. Machine Learning by Tom Mitchell
5. Computer Vision by Linda Shapiro and George Stockman
6. Speech and Language Processing by Daniel Jurafsky and James H. Martin

Practice

1. Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp by Peter Norvig
2. Prolog Programming for Artificial Intelligence Ivan Bratko
3. Programming Collective Intelligence by Toby Segaran
4. Programming the Semantic Web by Colin Evans, Jamie Taylor, and Toby Segaran
5. Introduction to Machine Learning with Python by Andreas C. Müller and Sarah Guido
6. Machine Learning with Python Cookbook by Chris Albon
7. Programming Computer Vision with Python by Jan Erik Solem
8. Natural Language Processing with Python by Edward Loper, Ewan Klein, and Steven Bird
9. Data Analysis with Open Source Tools. by Philipp K. Janert
10. Doing Data Science by Cathy O’Neil and Rachel Schutt
11. Hadoop: The Definitive Guide by Tom White
12. Spark: The Definitive Guide by Bill Chambers, Matei Zaharia
13. MongoDB: The Definitive Guide by Shannon Bradshaw, Eoin Brazil, Kristina Chodorow
14. Cassandra: The Definitive Guide by Eben Hewitt and Jeff Carpenter

Tools

• Scala
• Clojure
• Common Lisp
• Python: Scikit-learn, TensorFlow, Keras, Theano, PyTorch, NLTK
• R
• Hadoop
• Spark

Published Papers