10 important papers to get started with machine learning

by Matthias Bitzer machine-learning research education

Starting your journey in machine learning? These 10 papers provide essential foundations for understanding the field.

Introduction

The machine learning field has produced thousands of papers, but certain works stand out as foundational. This curated list helps newcomers build a solid theoretical foundation.

The Papers

1. Backpropagation (1986)

“Learning representations by back-propagating errors”
Rumelhart, Hinton, Williams

The paper that made training deep neural networks practical. Understanding backpropagation is essential for any deep learning work.

2. Support Vector Machines (1995)

“Support-Vector Networks”
Cortes, Vapnik

Introduced SVMs, demonstrating how to find optimal decision boundaries. Still relevant for understanding kernel methods and margin-based classification.

3. Random Forests (2001)

“Random Forests”
Leo Breiman

Ensemble learning that remains one of the most robust and interpretable methods. Essential for understanding bagging and feature importance.

4. Dropout (2014)

“Dropout: A Simple Way to Prevent Neural Networks from Overfitting”
Srivastava, Hinton, et al.

A remarkably simple yet effective regularization technique that’s now standard in deep learning.

5. Adam Optimizer (2014)

“Adam: A Method for Stochastic Optimization”
Kingma, Ba

The default optimizer for most deep learning projects. Understanding adaptive learning rates is crucial.

6. Batch Normalization (2015)

“Batch Normalization: Accelerating Deep Network Training”
Ioffe, Szegedy

Made training very deep networks practical by normalizing layer inputs.

7. ResNet (2015)

“Deep Residual Learning for Image Recognition”
He, Zhang, Ren, Sun

Introduced skip connections, enabling training of networks with hundreds of layers. Won ImageNet 2015.

8. Attention Is All You Need (2017)

“Attention Is All You Need”
Vaswani et al.

Introduced the Transformer architecture, revolutionizing NLP and beyond. Foundation for BERT, GPT, and modern LLMs.

9. BERT (2018)

“BERT: Pre-training of Deep Bidirectional Transformers”
Devlin et al.

Demonstrated the power of pre-training and transfer learning in NLP.

10. An Image is Worth 16x16 Words (2020)

“An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”
Dosovitskiy et al.

Vision Transformers (ViT) brought transformer success to computer vision.

How to Read Papers

  1. First pass: Read abstract, introduction, and conclusion
  2. Second pass: Understand the methodology and key figures
  3. Third pass: Implement or reproduce the results

Additional Resources

  • ArXiv: Latest preprints
  • Papers With Code: Papers with implementations
  • Connected Papers: Visualize paper relationships

Conclusion

These papers span three decades of progress. Reading them provides both historical context and practical knowledge that remains relevant today.