- Author: Mohammad Javad Maheronnaghsh
- Linear Regression
- Lasso & Ridge Regression
- Non-Linear (Polynomial) Regression
- Logistic Regression
- Decision Tree
- Bagging
- AdaBoost
- TF-IDF
- Neural Networks
- K-Means
- MSE (Mean Squared Error)
- Cross-Entropy
- Binary
- Categorical (Multi-class)
- Hinge Loss
- used in SVMs
- Logistic Loss
- used in logistic regression
- Logistic Regression is about classification, not regression!
- There are 3 classical regression models: Lasso, Ridge, Linear Regression.
- The cost functions are originated from MLE (Maximum Likelihood Estimator).
- For example: Cross-Entropy is originated from MLE of Bernoli Distribution, and MSE is originated from MLE of Normal Distribution.
- [Persian] ML in simple words: https://baridsoft.net/machine-learning-in-simple-words/
- ML Fields: https://paperswithcode.com/methods
- [Persian] Wikipedia Page - Dr. Sharifi Zarchi and Mr. Azarkhalili's course on ML
- What is ML in simple words
- Upload the lecture notes
- Upload useful slides
- Upload Useful assignments and answers
- Add links of useful courses (videos) and their assignments with answers
- The previous suggestion can be converted into a bank of questions that is useful for teaching assisstants to design homeworks
- add link of ML course (Dr. Motahari)