July 18, 2019

# snrazavi/Machine_Learning_2018

Codes and Project for Machine Learning Course, Fall 2018, University of Tabriz

repo name snrazavi/Machine_Learning_2018
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language Jupyter Notebook
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created 2018-10-16

# Machine Learning Course (Fall 2018)

Codes and Projects for Machine Learning Course, University of Tabriz.

# Contents:

## Supervised Learning

### Chapter 2: Regression

• Linear regression
• Multi-variable linear regression
• Polynomial regression (video)
• Normal equation
• Locally weighted regression
• Probabilistic interpretation (video)

### 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 (video)

• Classification and logistic regression
• Probabilistic interpretation
• Logistic regression cost function
• Logistic regression and gradient descent
• Multi-class logistic regression

### Chapter 5: Regularization (video)

• Overfitting and Regularization
• L2-Regularization (Ridge)
• L1-Regularization (Lasso)
• Regression with regularization
• Classification with regularization

### Chapter 6: Neural Networks (video)

• Milti-class logistic regression
• Softmax classifier
• Training softmax classifier
• Geometric interpretation
• Non-linear classification
• Neural Networks (video: part 2)
• Training neural networks: Backpropagation
• Training neural networks: advanced optimization methods (video: part 3)

### Chapter 7: Support Vector Machines

• Motivation: optimal decision boundary (video: part 1)
• Support vectors and margin
• Objective function formulation: primal and dual
• Non-linear classification: soft margin (video: part 2)
• Non-linear classification: kernel trick
• Multi-class SVM

## Unsupervided Learning

### Chapter 8: Clustering (video)

• Supervised vs unsupervised learning
• Clustering
• K-Means clustering algorithm (demo)
• Determining number of clusters: Elbow method
• Postprocessing methods: Merge and Split clusters
• Bisectioning clustering
• Hierarchical clustering
• Application 1: Clustering digits
• Application 2: Image Compression

### Chapter 9: Dimensionality Reduction and PCA (video)

• Introduction to PCA
• PCA implementation in python
• PCA Applications
• Singular Value Decomposition (SVD)
• Downloas slides in Persian (pdf)

### Chapter 10: Anomally Detection (video: Part 1, Part 2)

• Intoduction to anomaly detection
• Some applications (security, manufacturing, fraud detection)
• Anoamly detection using probabilitic modelling
• Uni-variate normal distribution for anomaly detection
• Multi-variate normal distribution for anomaly detection
• Evaluation measures (TP, FP, TN, FN, Precision, Recall, F-score)
• Anomaly detection as one-class classification
• Classification vs anomaly detection

### Chapter 11: Recommender Systems (video)

• Introduction to recommender systems
• Collaborative filtering approach
• User-based collaborative filtering
• Item-based collaborative filtering
• Similarity measures (Pearson, Cosine, Euclidian)
• Cold start problem
• Singular value decomposition
• Content-based recommendation
• Cost function and minimization