ML00-study preview
- 1 minStudy Preview
It’s just over a year since I started studying about data science. When I just began, I studied with this book, JAMES, Gareth, et al. An introduction to statistical learning. springer, 2013.
After this, I wanted to determine my research area. So I tried to understand several topics. I studied about 1) computer vision with CS231n, Stanford, 2) overall deep learning with Ian Goodfellow, Deep Learning, 3) stochastic process. Plus, I took several lectures for improving my background in statistics, linear algebra, and for enhancing programming skills.
Finally, I think I’m really interested in statistical learning and its various application. Thus, I want to study deeper about Statistical Machine Learning and summarize on my Github page.
Study Reference
The main reference is Christopher Bishop, Pattern Recognition and Machine Learning. Springer 2007.
But my summary does not strictly follow the order of the textbook.
I will also reference the following books & lectures to further extend the depth and breadth of my knowledge of statistical machine learning.
- Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning. Springer 2008.
- IIE661, I.C. Moon. KAIST
- Bayesian Deep Learning, S.J. Choi., Edwith
Study Topics
- Classic Machine Learning Models
- Linear Regression
- Tree based model(Decision Tree, Random Forest, Boosting Model)
- Logistic Regression, Naive Bayes Classifier
- SVM and kernel
- Neural Networks
- Sampling Method
- LDA and GP
- Bayesian Optimization
- Variational Inference
- Bayesian Neural Networks