Sunghee Yun

Co-founder & CTO @ Erudio Bio, Inc., KFAS-Salzburg Global Leadership Initiative Fellow @ Salzburg Global Seminar, Adjunct Professor @ Sogang University, Advisory Professor @ DGIST

Online class sites

Machine learning (ML)

Books

  • Introduction to Applied Linear Algebra - Stephen Boyd @ Stanford University, Lieven Vandenberghe @ UCLA (Amazon, Boyd's homepage, PDF)
  • Deep Learning - Ian Goodfellow, Yoshua Bengio, Aaron Courville (Amazon, PDF)
  • Pattern Recognition and Machine Learning (Information Science and Statistics) - Christopher M. Bishop (Amazon, PDF)
  • Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) - Daphne Koller, Nir Friedman (Amazon)
  • Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) - Kevin P. Murphy (Amazon)
  • Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series) - Kevin P. Murphy (Amazon)
  • Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series) - Richard S. Sutton, Andrew G. Barto (Amazon, PDF)
  • The Elements of Statistical Learning: Data Mining, Inference, and Prediction - Trevor Hastie, Robert Tibshirani, Jerome Friedman (Amazon)  
    my comment

    The bible of traditional statistical learning. Very hard to read. I do not recommend for beginners.

    However, if you're versed with statistics and want to understand the core of (classical) theory and development, you may want to skim through it or/and use it as a reference for have statistically or mathematically rigorous understanding of basic and advanced concepts such as overfitting, the expectation-maximization (EM) algorithm, and Bayesian inference.

Lectures

  • (coursera) Machine Learning Specialization - Andrew Ng @ Stanford University & DeepLearning.AI, Geoff Ladwig @ DeepLearning.AI, Aarti Bagul
    • Supervised Machine Learning: Regression and Classification
    • Advanced Learning Algorithms
    • Unsupervised Learning, Recommenders, Reinforcement Learning
  • (coursera) Deep Learning Specialization - Andrew Ng @ Stanford University & DeepLearning.AI, Younes Bensouda Mourri @ DeepLearning.AI, Kian Katanforoosh @ DeepLearning.AI
    • Neural Networks and Deep Learning
    • Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
    • Structuring Machine Learning Projects
    • Convolutional Neural Networks
    • Sequence Models

Optimization

Books