# 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

## Assignments:

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

Get A Weekly Email With Trending Projects For These Topics
No Spam. Unsubscribe easily at any time.
Python (806,773
Jupyter Notebook (154,126
Learning (76,219
Machine Learning (37,065
Neural (16,728
Neural Network (15,492
Slides (13,503
Classification (13,261
Recommender System (2,813
Pca (2,053
Linear Regression (1,684
Logistic Regression (1,557
Regularization (1,146
Persian (1,081
Anomaly Detection (1,026