Latest Update: 11.05.2020
Credit: this repository is based on awsome works
Material (Il-Chul Moon, KAIST)
- Introduction to Artificial Intelligence and Machine Learning I
- Introduction to Artificial Intelligence and Machine Learning II
- Artificial Intelligence and Machine Learning (Advanced)
https://kooc.kaist.ac.kr/
Code (Introduction to Artificial Intelligence and Machine Learning I)
- MLE: https://zhiyzuo.github.io/MLE-vs-MAP/
- MAP: https://zhiyzuo.github.io/MLE-vs-MAP/
- Decision Tree: https://github.com/gilbutITbook/007022/blob/master/code/ch03/ch03.ipynb
- Naive Bayes Classifier: https://jakevdp.github.io/PythonDataScienceHandbook/05.05-naive-bayes.html
- Linear Regression: https://github.com/gilbutITbook/007022/blob/master/code/ch10/ch10.ipynb
- Logistic Regression: https://github.com/Jeiyoon/007022/blob/master/code/ch03/ch03.ipynb
- Support Vector Machine: https://github.com/Jeiyoon/007022/blob/master/code/ch03/ch03.ipynb
- Training/Testing and Regularization: https://github.com/Jeiyoon/007022/blob/master/code/ch06/ch06.ipynb
Code (Introduction to Artificial Intelligence and Machine Learning II)
- Bayesian Network: https://github.com/pgmpy/pgmpy_notebook/blob/master/notebooks/9.%20Learning%20Bayesian%20Networks%20from%20Data.ipynb
- K-Means Clustering and Gaussian Mixture Model: TBA
- Hidden Markov Model: TBA
- Sampling Based Inference: TBA
Code (Artificial Intelligence and Machine Learning (Advanced))
- Dirichlet Process: TBA
- Gaussian Process: TBA
- Variational Inference: TBA
- Artificial Neural Network: TBA