Essential AI 101: Bridging the Knowledge Gap: Data Skills and the Importance of Data in AI - Episode 3

Reference List

1. Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
2. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
3. Kelleher, J. D. (2019). Deep Learning (MIT Press Essential Knowledge Series).
4. Domingos, P. (2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books.

Additional Resources

1. Google’s Machine Learning Crash Course – https://developers.google.com/machine-learning/crash-course
2. IBM Data Science Professional Certificate (Coursera) – https://www.coursera.org/professional-certificates/ibm-data-science
3. Stanford’s CS229: Machine Learning Course – https://cs229.stanford.edu/
4. Tableau Public – https://public.tableau.com/
5. The Elements of AI (University of Helsinki) – https://www.elementsofai.com/
6. Google Cloud BigQuery for SQL & Data Analysis – https://cloud.google.com/bigquery

Additional Readings

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.