My top 10 references for studying Applied Mathematics & Computer Science during COVID-19 pandemic
While more than 850 million students around the world have to stay at home because of the COVID-19 pandemic, free access to high quality online resources is more necessary than ever. After studying for 3 years fields such as Machine Learning, Computer Vision, Stochastic Calculus, Linear and Convex Optimization and Game Theory, I am sharing with you my top 10 references in these fields of Applied Mathematics and Computer Science.
These must-have references in their respective fields have the triple advantage of being quite exhaustive for a first learning experience, of explaining the different notions clearly and often with illustrations, and finally of being elegantly presented, which is essential when tackling the reading of a book of several hundred pages!
Students, former students and professors from all horizons, do not hesitate to react to this list and to share your open access resources in your areas of specialization!
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. Trevor Hastie, Robert Tibshirani and Jerome Friedman, 2009.
- Deep Learning. Ian Goodfellow, Yoshua Bengio and Aaron Courville, 2016.
- Reinforcement Learning: An Introduction, Second Edition. Richard S. Sutton and Andrew G. Barto, 2018.
- Computer Vision: Algorithms and Applications. Richard Szeliski, 2010.
- Speech and Language Processing, Third Edition. Dan Jurafsky and James H. Martin, 2019.
- An Introduction to Stochastic Calculus with Applications to Finance. Ovidiu Calin, 2012.
- Introduction to Time Series and Forecasting, Second Edition. Peter J. Brockwell and Richard A. Davis, 2002.
- Operations Research: A Practical Introduction, Second Edition. Michael W. Carter, Camille C. Price, and Ghaith Rabadi, 2018.
- Convex Optimization. Stephen Boyd and Lieven Vandenberghe, 2009.
- Game Theory, Second Edition. Giacomo Bonanno, 2018.
Bonus in Computer Science:
- Introduction to Algorithms, Third Edition. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein, 2009.
