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Jupyter Notebook tutorials for the Technion's CS 236756 course "Introduction to Machine Learning"
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Jupyter Notebook tutorials for the Technion's CS 236756 course "Introduction to Machine Learning"

Running The Notebooks

You can view the tutorials online or download and run locally.

Running Online

Service Usage
Jupyter Nbviewer Render and view the notebooks (can not edit)
Binder Render, view and edit the notebooks (limited time)
Google Colab Render, view, edit and save the notebooks to Google Drive (limited time)

Jupyter Nbviewer:


Press on the "Open in Colab" button below to use Google Colab:

Open In Colab

Or press on the "launch binder" button below to launch in Binder:


Note: creating the Binder instance takes about ~5-10 minutes, so be patient

Running Locally

Press "Download ZIP" under the green button Clone or download or use git to clone the repository using the following command: git clone (in cmd/PowerShell in Windows or in the Terminal in Linux/Mac)

Open the folder in Jupyter Notebook (it is recommended to use Anaconda). Installation instructions can be found at the bottom of the README file.


File Topics Covered
cs236756_tutorial_01_probability_mle.ipynb\pdf Probability basics, random variables, Bayes rule, histograms, correlation, parameter estimation, Maximum Likelihood Estimation (MLE)
cs236756_tutorial_02_statistics.ipynb\pdf Statistics definitions, hypothesis testing steps, z-statistic, Central Limit Theorem (CLT), Area Under the Curve (AUC), error types, confusion matrix
cs236756_tutorial_03_linear_algebra.ipynb\pdf Linear Algebra basics (vectors, inner/outer product spaces, norms, linear dependency, matrix operations, matrix rank, range and nullspace), least-squares solution, eigenvalues and eigenvectors, Singuar Value Decomposition (SVD)
cs236756_tutorial_04_pca_feature_selection.ipynb\pdf Dimensionality Reduction, Outliers, PCA, SVD, Breast Cancer dataset, Feature Selection, Filter methods, Wrapper methods, RFE (scikit-learn)
cs236756_tutorial_05_evaluation_validation.ipynb\pdf Classifier Evaluation and Validation, metrics, accuracy, precision, recall, FN/TP rate, Confusion Matrix, F1 score, K-Fold Cross-Validation, train-validation-test split, holdout method, stratification, ROC curve
cs236756_tutorial_06_optimization.ipynb\pdf Optimization in ML, Gradient Descent, Batch Gradient Descent, Mini-Batch (MB) Gradient Descent, Stochastic Gradient Descent (SGD), Convexity, Uni/Multi-modal problems, Lagrangian and Largrange Multipliers, Constrained Optimization
cs236756_tutorial_07_linear_regression.ipynb\pdf Classification vs. Regression, NLL (Negative Log-Likelihood), MLE connection to MSE, Residual Analysis, Basis Functions Expansion, Feature Extraction, Linear and Polynomial Regression, Bias-Variance Tradeoff, Irreducible Error, Regularization (L1 + L2), Ridge and LASSO Regression
cs236756_tutorial_08_linear_model.ipynb\pdf Discriminative vs Generative Models, Linear Models, Perceptron, Least Mean Square (LMS) - Adaptive Linear Neuron (ADALINE), MLE with Bernoulli, Logistic Regression, Softmax, Maximum A Posteriori (MAP), Quadratic Discriminant Analysis (QDA), Naive Bayes, Linear Discriminant Analysis (LDA), One-vs-All Classification
cs236756_tutorial_09_expectation_maximization.ipynb\pdf Soft Clustering, Hard Clustering, K-Means, Expectation Maximization (EM) Algorithm, Gaussian Mixture Model (GMM), Bernoulli Mixture Model (BMM), Dataset Generation with Scikit-Learn
cs236756_tutorial_10_boosting_bagging.ipynb\pdf Ensemble Learning, Voting Classifiers, Hard Voting, Soft Voting, Random Forests, Bagging, Pasting, Bootstrap, Boosting, AdaBoost
cs236756_tutorial_11_svm.ipynb\pdf Support Vector Machine (SVM), Linear SVM, Hard/Soft SVM, The Primal Problem, The Dual Problem, The Kernel Trick, Kernel SVM, RBF Kernel, Polynomial Kernel, The Mercer Condition
cs236756_tutorial_12_deep_learning_intro_backprop.ipynb\pdf Deep Learning Introduction, The XOR Problem, Multi-Layer Perceptron (MLP), Backpropagation, Activation Functions: Sigmoid, Tanh, ReLU, Forward Pass, Backward Pass, Boston Housing Dataset
cs236756_tutorial_13_pac_vc_dimension.ipynb\pdf Probably Approximately Correct (PAC) Learning, Risk, Empirical Risk, Empirical Risk Minimization (ERM), Inductive Bias, VC Dimension, Shattering, Dichotomy, No Free Lunch Theorem
cs236756_tutorial_OX_decision_trees.ipynb\pdf Decision Trees, The CART algorithm, Prunning, Regularization, Impurity Metrics, Entropy, Gini, Information Gain (IG), SplitInformation, Gain Ratio (GR), The Titanic Dataset, Tree Visualization with Scikit-Learn
cs236756_exam_perparation.pdf Exam Preparations Guidelines, Exam Questions Repository (External Sources)

Installation Instructions

  1. Get Anaconda with Python 3, follow the instructions according to your OS (Windows/Mac/Linux) at:
  2. Create a new environment for the course (full guide at In Windows open Anaconda Prompt from the start menu, in Mac/Linux open the terminal and run conda create --name ml_course
  3. To activate the environment, open the terminal (or Anaconda Prompt in Windows) and run conda activate ml_course
  4. Install the required libraries according to the table below (to search for a specific library and the corresponding command you can also look at

Libraries to Install

Library Command to Run
Jupyter Notebook conda install -c conda-forge notebook
numpy conda install -c conda-forge numpy
matplotlib conda install -c conda-forge matplotlib
pandas conda install -c conda-forge pandas
scipy conda install -c anaconda scipy
scikit-learn conda install -c conda-forge scikit-learn
  1. To open the notbooks, run jupyter notebook in the terminal (or Anaconda Prompt in Windows) while the ml_course environment is activated.
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