Subareas
- Knowledge Representation
- Machine Learning
- Computer Vision
- Natural Language Processing
- Big Data, Data Science, Data Mining
Books
Theory
- Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig
- Artificial Intelligence by Elaine A. Rich and Kevin Knight
- Artificial Intelligence by Patrick Winston
- Machine Learning by Tom Mitchell
- Computer Vision by Linda Shapiro and George Stockman
- Speech and Language Processing by Daniel Jurafsky and James H. Martin
Practice
- Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp by Peter Norvig
- Prolog Programming for Artificial Intelligence Ivan Bratko
- Programming Collective Intelligence by Toby Segaran
- Programming the Semantic Web by Colin Evans, Jamie Taylor, and Toby Segaran
- Introduction to Machine Learning with Python by Andreas C. Müller and Sarah Guido
- Machine Learning with Python Cookbook by Chris Albon
- Programming Computer Vision with Python by Jan Erik Solem
- Natural Language Processing with Python by Edward Loper, Ewan Klein, and Steven Bird
- Data Analysis with Open Source Tools. by Philipp K. Janert
- Doing Data Science by Cathy O’Neil and Rachel Schutt
- Hadoop: The Definitive Guide by Tom White
- Spark: The Definitive Guide by Bill Chambers, Matei Zaharia
- MongoDB: The Definitive Guide by Shannon Bradshaw, Eoin Brazil, Kristina Chodorow
- 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