Objective: The objective of this assignment is to provide students with a
comprehensive understanding of various machine learning techniques, their
applications, advantages, and limitations. Students will explore different
algorithms, their implementations, and gain hands-on experience through
practical exercises.
Assignment Components:
1. Introduction to Machine Learning (ML):
• Define machine learning and its significance in various fields.
• Explain the difference between supervised, unsupervised, and
reinforcement learning.
• Provide real-world examples of machine learning applications.
2. Supervised Learning:
• Define supervised learning and its types (classification and
regression).
• Introduce popular supervised learning algorithms such as:
• Linear Regression
• Logistic Regression
• Support Vector Machines (SVM)
• Decision Trees
• Random Forests
• Explain the process of model training, evaluation, and prediction.
3. Unsupervised Learning:
• Define unsupervised learning and its types (clustering and
dimensionality reduction).
• Introduce popular unsupervised learning algorithms such as:
• K-Means Clustering
• Hierarchical Clustering
• Principal Component Analysis (PCA)
• t-Distributed Stochastic Neighbor Embedding (t-SNE)
• Discuss use cases and applications of unsupervised learning.
4. Evaluation Metrics:
• Explain common evaluation metrics for classification and
regression models:
• Classification: Accuracy, Precision, Recall, F1-score, ROC
Curve, AUC-ROC.
• Regression: Mean Squared Error (MSE), Root Mean Squared
Error (RMSE), Mean Absolute Error (MAE), R-squared.
• Illustrate how to interpret evaluation metrics and choose
appropriate metrics based on the problem domain.
5. Feature Engineering:
• Define feature engineering and its importance in machine
learning.
• Discuss techniques for feature preprocessing, including:
• Handling missing values
• Feature scaling
• Feature encoding (one-hot encoding, label encoding)
• Feature transformation (log transformation, polynomial
features)
• Provide examples demonstrating feature engineering in practice.
6. Model Selection and Hyperparameter Tuning:
• Explain the concept of model selection and hyperparameter
tuning.
• Introduce techniques such as:
• Cross-validation
• Grid Search
• Random Search
• Demonstrate how to use these techniques to improve model
performance.
7. Case Study:
• Provide a real-world dataset and a problem statement.
• Instruct students to perform the following tasks:
• Data preprocessing and exploration.
• Model selection and training using appropriate algorithms.
• Evaluation of model performance.
• Interpretation of results and potential areas for
improvement.
8. Conclusion:
• Summarize key concepts covered in the assignment.
• Discuss the importance of continuous learning and
experimentation in machine learning.
Deliverables:
• A written report documenting the solutions to each component of the
assignment.
• Code implementations (in a programming language of choice,
preferably Python) for practical exercises.
• Presentation slides summarizing key findings and results of the case
study.
Assessment Criteria:
• Understanding of machine learning concepts.
• Clarity and completeness of the written report.
• Correctness and efficiency of code implementations.
• Ability to interpret and analyze model performance.
• Creativity and critical thinking demonstrated in the case study.
References:
• Textbooks, online resources, and research papers covering machine
learning concepts and techniques.
• Documentation and tutorials for libraries/frameworks such as scikit-
learn, TensorFlow, and PyTorch.
This assignment is designed to provide students with a holistic understanding
of machine learning, from theoretical concepts to practical implementation. It
aims to equip them with the necessary knowledge and skills to tackle real-
world problems using machine learning techniques.
