This page provides the recommended learning resources for the course, including primary textbooks, reference books, software tools, online learning platforms, and additional resources to support classroom learning and self-study.
Primary Textbooks : The following textbooks serve as the primary references for the course and closely align with the lecture content.
Reference Books : Students interested in exploring topics beyond the classroom are encouraged to consult the following references.
Thomas H. Davenport and Jeanne G. Harris, Competing on Analytics: The New Science of Winning, Updated Edition, Harvard Business Review Press, 2017.
Christoph Molnar, Interpretable Machine Learning, 3rd Edition, 2025 (Open Access). [Pdf Link]
Cathy O'Neil, Weapons of Math Destruction, Crown Publishing, 2016. [Pdf Link]
Brian Christian, The Alignment Problem: Machine Learning and Human Values, W. W. Norton & Company, 2020. [Pdf Link]
Software & Computational Tools : The following software will be used throughout the course for demonstrations, visualization, and computational experiments.
Logisim-evolution : https://sourceforge.net/projects/logisimevolution/
NetworkX (Python) : https://pypi.org/project/networkx/
Gepi : https://gephi.org/
Graphviz : https://graphviz.org/
Z3 Theorem Prover : https://github.com/z3prover/z3
Neo4j : https://neo4j.com/
Online Learning Resources : The following resources provide additional explanations, interactive examples, and practice materials.