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

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


Chapter 1: Introduction (video)

  • download slides in Persian (pdf)

Supervised Learning

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 (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)

Furthur Reading

Chapter 5: Regularization (video)

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

Furthur Reading

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)
  • Gradient checking
  • Mini-batch gradient descent
  • Download slides in Persian (pdf)


Related Videos:

Free Online Books:

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
  • Download slides in persian (pdf)


Furthur Reading

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
  • Download slides in Persian (pdf)

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
  • Download slides in Persian (pdf)

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
  • Download slides in Persian (pdf)

Other Useful Resources


  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
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