Reference List
- Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
- Kelleher, J. D. (2019). Deep Learning (MIT Press Essential Knowledge Series).
- Domingos, P. (2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books.
Additional Resources
- Google’s Machine Learning Crash Course – https://developers.google.com/machine-learning/crash-course
- IBM Data Science Professional Certificate (Coursera) – https://www.coursera.org/professional-certificates/ibm-data-science
- Stanford’s CS229: Machine Learning Course – https://cs229.stanford.edu/
- Tableau Public – https://public.tableau.com/
- The Elements of AI (University of Helsinki) – https://www.elementsofai.com/
- Google Cloud BigQuery for SQL & Data Analysis – https://cloud.google.com/bigquery
Additional Readings
- Gebru, T., et al. (2018). "Datasheets for Datasets." arXiv preprint arXiv:1803.09010.
- Discusses ethical AI data collection and how biases in training data impact AI outcomes.
- Ng, A. (2018). "AI Transformation Playbook." Landing AI.
- A guide to how businesses can adopt AI, focusing on the role of data preparation and model training.
- LeCun, Y., Bengio, Y., & Hinton, G. (2015). "Deep Learning." Nature, 521(7553), 436–444.
- Covers the foundations of deep learning and how AI models learn from massive datasets.
- Cathy O'Neil (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy.
- A critical look at AI bias, data ethics, and the real-world consequences of bad data.
- Dwork, C., & Mulligan, D. K. (2013). "It’s Not Privacy, and It’s Not Fair." Stanford Law Review Online, 66, 35.
- Explores the privacy and fairness implications of AI-driven decision-making.
- Pasquale, F. (2020). New Laws of Robotics: Defending Human Expertise in the Age of AI.
- Discusses the ethical and societal impact of data-driven AI decision-making.
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