Culture

Characteristics

• Focusing on Fundamentals

  • 1. Recommended (Basic) Textbooks

• Bishop, Christopher, Pattern Recognition and Machine Learning. Springer.

• Boyd, S., & Vandenberghe, L., Convex optimization. Cambridge university press.

• Dimitri P. Bertsekas, Convex Optimization Theory, Athena Scientific

• Gene Golub and Charles Van Loan, Matrix Computation, Johns Hopkins Press

• Michael R. Garey, Computers and Intractability: A Guide to the Theory of NP-Completeness

• Goodfellow, Y. Bengio, and A. Courville. Deep Learning, MIT Press.

• Chi-Tsong Chen, Linear system theory and design, Oxford University Press

• T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Springer

• Thomas M. Cover, Joy A. Thomas, Elements of Information Theory, Wiley

• J. Pearl, Causal Inference in Statistics - A Primer, Wiley

• Stuart Russell & Peter Norvig, Artificial Intelligence: A Modern Approach, Pearson

• Kevin Patrick Murphy, Probabilistic Machine Learning: An Introduction, MIT Press

• Hassan K. Khalil, Nonlinear Control, Prentice Halll

• Carl Edward Rasmussen and Christopher K. I. Williams, Gaussian Processes for Machine Learning, The MIT Press

• Daniel Kahneman, Judgment Under Uncertainty: Heuristics and Biases. Cambridge University Press

• • 2. Recommended (Basic) Skills

     (You may be familiar with one of those languages or skills)

• Python (including Pytorch, Tensorflow, or ONNX)

• MATLAB (and Simulink)

• R (and SAS), Julia

• Scala, Spark, Hadoop

• C++ (and/or CUDA)

• Self-motivated, Proactive

• We can explore anything with trials-and-errors. Errors contain meaningful information in terms of Active Learning. 

• Interests in Biomedical/clinical knowledge and engineering, clinical practices or healthcare systems and applications. 

• How can we connect ML/AI models and real-world (real-life) in terms of clinical/healthcare contexts?

• We should focus on deep methodologies for generalization, even we can adopt this methods to not only biomedical fields. 

• From study to real-world, from benchside to bedside, between research and development.