Course materials and learning resources for Machine Learning, including lecture materials, practical exercises, assignments, examinations, model answers, textbooks, references, datasets, code examples, and supplementary learning resources.
Table Of Content
Course Information
| Course Information | Details |
|---|---|
| Course Title | Machine Learning |
| Course Code | ML |
| Level | Undergraduate |
| Current Semester | First Semester |
| Academic Year | 2026–2027 |
| Instructor | Prof. Mostafa Elhosseini |
| Primary Textbook | Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition by Aurélien Géron Grokking Machine Learning — Luis G. Serrano |
Current Semester — 2026–2027
Course resources for the current semester are provided below and will be updated throughout the semester as new lectures, practical activities, assignments, and assessment materials become available.
Lecture Materials
| Lecture | Topic | Slides | Handout |
|---|---|---|---|
| 1 | What is Machine Learning | ||
| 2 | Big Picture | ||
| 3 | Machine Learning Applications | ||
| 4 | ML Workflow | ||
| 5 | ML Types Supervised – Classification Regression | ||
| 6 | ML Types Unsupervised | ||
| 7 | ML Types Semi and self Supervised Learning | ||
| 8 | ML Types – Reinforcement Learning | ||
| 9 | Batch / Online ML | ||
| 10 | Model-based / Instance-based | ||
| 11 | Main challenges | ||
| 12 | Main challenges – Bad Data | ||
| 13 | Main challenges – Overfitting | ||
| 14 | Main challenges – Underfitting | ||
| 15 | Hyperparameter tuning and Model selection | ||
| 16 | End-to-End ML Project | ||
| 17 | Colab | ||
| 18 | A Quick Dive into Dataset Structures | ||
| 19 | Creating Test Set | ||
| 20 | Train/Test Split | ||
| 21 | Discover and Visualize the Data to Gain Insights | ||
| 22 | Prepare Data for ML | ||
| 23 | Handling Text and Categorical Attributes | ||
| 24 | Feature Scaling and Transformation | ||
| 25 | Scikit Learn Design | ||
| 26 | Custom Transformers | ||
| 27 | Transformation Pipelines | ||
| 28 | Select and Train a Model | ||
| 29 | Fine Tune Your Model | ||
| 30 | Randomized Search | ||
| 31 | Sampling Distribution | ||
| 32 | Hyperparameter Sampling Distribution | ||
| 33 | Analyzing the Best Models and Their Errors | ||
| 34 | Evaluate Your System on the Test Set | ||
| 35 | Launch, Monitor, and Maintain Your System | ||
| 36 | Classification | ||
| 37 | Performance Measures | ||
| 38 | Confusion Matrix | ||
| 39 | Precision and Recall | ||
| 40 | Precision/Recall Trade-off | ||
| 41 | Receiver Operating Characteristic (ROC) | ||
| 42 | Multiclass Classification | ||
| 43 | Error Analysis | ||
| 44 | Multilabel Classification | ||
| 45 | Multioutput Classification | ||
| 46 | Training Models | ||
| 47 | Simple – Square – Absolute Trick | ||
| 48 | Square – Absolute Trick | ||
| 49 | Error Functions – Healthcare Application | ||
| 50 | Gradient Descent | ||
| 51 | Polynomial Regression | ||
| 52 | Learning Curves | ||
| 53 | Regularization | ||
| 54 | Ridge Regularization | ||
| 55 | Lasso & Elastic Net Regularization | ||
| 56 | Early Stopping | ||
| 57 | Perceptron | ||
| 58 | Perceptron – Error Function | ||
| 59 | Perceptron Algorithm | ||
| 60 | Perceptron Applications – Spam filters | ||
| 61 | Logistic Regression | ||
| 62 | Logistic Regression – Implementation | ||
| 63 | Softmax Regression | ||
| 64 | Softmax Regression – Implementation | ||
| 65 | Support Vector Classifier (SVC) | ||
| 66 | SVC Error Function | ||
| 67 | Kernel Trick | ||
| 68 | Polynomial Kernel | ||
| 69 | Radial Basis Function (RBF) Kernel | ||
| 70 | Radial Basis Function (RBF) Kernel – Cont. | ||
| 71 | Decision Tree | ||
| 72 | Decision Tree: Visualizing CART | ||
| 73 | Gini Impurity Index | ||
| 74 | Entropy | ||
| 75 | How to avoid Overfitting? | ||
| 76 | How to Split based on Continuous Feature | ||
| 77 | Regression using DT | ||
| 78 | Real Life Application | ||
| 79 | Ensemble Learning | ||
| 80 | Bootstrap – Aggregate (Bagging) | ||
| 81 | Out-of-Bag Evaluation – Random Patches and Random Subspaces | ||
| 82 | Random Forest | ||
| 83 | Boosting | ||
| 84 | Gradient Boosting – XGBoost | ||
| 85 | Chapter 08 – Dimensionality Reduction (to be continued) | DOC |
Practical Activities
| Activity | Topic | Notebook / Code | Supporting Files |
|---|---|---|---|
| 1 | Python ML Environment | Code | Files |
| 2 | Data Preprocessing | Notebook | Dataset |
| 3 | Linear Regression | Notebook | Dataset |
| 4 | Classification | Notebook | Dataset |
| 5 | Decision Trees | Notebook | Dataset |
| 6 | Model Evaluation | Notebook | Dataset |
Assignments
| Assignment | Topic | Due Date | Download |
|---|---|---|---|
| Assignment 1 | Regression | — | |
| Assignment 2 | Classification | — | |
| Assignment 3 | Model Evaluation | — | |
| Assignment 4 | Machine Learning Project | — |
Exams
Sample / Practice Exams
| Resource | Download |
|---|---|
| Sample Midterm Exam | |
| Sample Final Exam |
Previous Exams
| Academic Year | Assessment | Download |
|---|---|---|
| 2025–2026 | Midterm | |
| 2025–2026 | Final |
Students are encouraged to attempt the questions independently before consulting the model answers.
Model Answers
| Resource | Model Answer |
|---|---|
| Sample Midterm Exam | |
| Sample Final Exam | |
| Selected Exercises | |
| Assignment Solutions |
Datasets & Code
| Resource | Description | Download |
|---|---|---|
| Dataset 1 | Dataset used for regression examples | Dataset |
| Dataset 2 | Classification dataset | Dataset |
| Python Code | Lecture examples | Code |
| Jupyter Notebooks | Practical implementations | Notebook |
Textbook & References
Primary Textbook
- Aurélien Géron — Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
- Luis G. Serrano
Grokking Machine Learning
Manning Publications.
Supplementary References
- Christopher M. Bishop — Pattern Recognition and Machine Learning
- Kevin P. Murphy — Machine Learning: A Probabilistic Perspective
Video Lectures
Watch the complete Machine Learning lecture series and supporting tutorials directly on this website.
Machine Learning
Video lectures covering fundamental Machine Learning concepts, algorithms, model training, evaluation, and practical applications.
Last Update:
September 3, 2026
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