Machine_Learning_2018

Codes and Projects for Machine Learning Course, University of Tabriz (Fall 2018).

• Course Website
• YouTube Videos (in Persian)

Contents:

Chapter 1: Introduction

• download slides in Persian (pdf) (video)

Chapter 2: Regression

• 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)

Chapter 3: Python and NumPy

• 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)

Chapter 4: Logistic Regression

• 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)

Furthur Reading

• 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

Chapter 5: Regularization

• Overfitting and Regularization
• L2-Regularization (Ridge)
• L1-Regularization (Lasso)
• Regression with regularization
• Classification with regularization
• Download slides in Persian (pdf) (video)

Furthur Reading

• The Elements of Statistical Learning: Data Mining, Inference, and Prediction, pages 61-68

Chapter 6: Neural Networks

Chapter 7: Support Vector Machines

Chapter 8: Clustering

Chapter 9: PCA

Chapter 10: Anomally Detection

Chapter 11: Recommender Systems

Other Useful Resources

• Optimization: Convex Optimization, Stephan Boyd, Stanford
• Linear algebra: pdf
• Calculus: Khan Accademy
• Probability: Khan Accademy

Assignments:

1. Regression and Gradient Descent
2. Classification, Logistic Regression and Regularization
3. Multi-Class Logistic Regression
4. Neural Networks Training
5. Neural Networks Implementing
6. Clustering
7. Dimensionallity Reduction and PCA
8. Recommender Systems