Codes and Projects for Machine Learning Course, University of Tabriz (Fall 2018).
- Linear regression
- Gradient descent algorithm (video)
- Multi-variable linear regression
- Polynomial regression (video)
- Normal equation
- Locally weighted regression
- Probabilistic interpretation (video)
- Download slides in Persian (pdf)
- Python basics
- Creating vectors and matrices in
numpy - Reading and writing data from/to files
- Matrix operations (video)
- Colon (:) operator
- Plotting using
matplotlib(video) - Control structures in python
- Implementing linear regression cost function (video)
- Classification and logistic regression
- Probabilistic interpretation
- Logistic regression cost function
- Logistic regression and gradient descent
- Multi-class logistic regression
- Advanced optimization methods
- Download slides in Persian (pdf) (video)
- Artificial Intelligence: A Modern Approach (3rd Edition), pages 725-727
- An Introduction to Statistical Learning: with Applications in R, pages 130-137
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, pages 119-128
- Overfitting and Regularization
- L2-Regularization (Ridge)
- L1-Regularization (Lasso)
- Regression with regularization
- Classification with regularization
- Download slides in Persian (pdf) (video)
- Optimization: Convex Optimization, Stephan Boyd, Stanford
- Linear algebra: pdf
- Calculus: Khan Accademy
- Probability: Khan Accademy
- Regression and Gradient Descent
- Classification, Logistic Regression and Regularization
- Multi-Class Logistic Regression
- Neural Networks Training
- Neural Networks Implementing
- Clustering
- Dimensionallity Reduction and PCA
- Recommender Systems