Category 1: Generative AI
Ian Goodfellow et al. (2014). “Generative Adversarial Nets.” Advances in Neural Information Processing Systems (NeurIPS).
The seminal paper that introduced GANs, laying the foundation for a myriad of generative models and approaches to producing synthetic data.
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Diederik P. Kingma & Max Welling (2013). “Auto-Encoding Variational Bayes.”
Introduced the concept of VAEs, a cornerstone generative framework that combines probabilistic modeling with neural networks, widely used for dimensionality reduction, anomaly detection, and synthetic data generation.
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Vaswani, Ashish et al. (2017): "Attention is All You Need."
Introduced the Transformer architecture, which has become the foundation for modern NLP systems like BERT and GPT.
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Volodymyr Mnih et al. (2015): "Human-level control through deep reinforcement learning."
Introduced Deep Q-Learning, demonstrating human-level performance on Atari games.
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Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin (2016): "Why Should I Trust You? Explaining the Predictions of Any Classifier."
Proposed LIME, a method for explaining the predictions of machine learning models.
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Yann LeCun et al. (1998): "Gradient-based learning applied to document recognition."
Introduced convolutional neural networks (CNNs) and their application to handwritten digit recognition.
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Cortes, Corinna, and Vladimir Vapnik (1995): "Support-vector networks."
Introduced the concept of SVMs, a widely used algorithm for classification and regression tasks.
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Leo Breiman (2001): "Random Forests."
Introduced the Random Forest algorithm, an ensemble method combining bagging and decision trees.
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Category 2: Machine Learning
Muller and Guido (2017): Introduction to Machine Learning With Python (Ebook)
Introduction to machine learning concepts and applications using Python.
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Ayodele, Taiwo Oladipupo (2016): "Machine learning overview."
An overview of machine learning concepts and their applications.
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Mitchell, Tom. M. (2017): "Key Ideas in Machine Learning"
Exploration of foundational concepts in machine learning, including key ideas and methodologies.
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Rudin, Cynthia (2019): "Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead."
Discussion on the importance of interpretable models over black-box methods in critical applications.
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Dietterich, Thomas G. (2002): "Ensemble Learning."
An overview of ensemble methods, including bagging, boosting, and their practical applications.
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Hastie, Trevor, Robert Tibshirani, and Jerome Friedman (2016): Chapters 3, 5, 9, and 10
Advanced topics in data mining and machine learning, covering linear models, ensemble methods, and more.
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Khurana, D., Koli, A., Khatter, K., & Singh, S. (2017): "Natural language processing: State of the art, current trends and challenges."
A comprehensive review of NLP techniques, challenges, and trends.
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Blei, David M., and John D. Lafferty (2006): "Dynamic topic models."
An introduction to dynamic topic modeling, its methodology, and applications in machine learning.
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