BSCS_CIS402: Machine Learning

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About Course

  • This course explores machine learning algorithms and applications, focusing on supervised and unsupervised learning techniques.
  • Students will learn the principles of machine learning, including model selection, training, evaluation, and optimization.
  • The course covers a range of algorithms, such as linear regression, decision trees, support vector machines, clustering, and neural networks, with hands-on experience using popular machine learning libraries.

What Will You Learn?

  • 1. Implement machine learning models for both regression and classification tasks.
  • 2. Apply unsupervised learning techniques such as clustering and dimensionality reduction.
  • 3. Evaluate and optimize models using cross-validation, grid search, and hyperparameter tuning.
  • 4. Understand and apply advanced machine learning techniques, including ensemble methods and deep learning.
  • 5. Analyze and interpret the results of machine learning experiments in the context of real- world applications.

Course Content

Module I

  • Week 1: Introduction to Machine Learning
    00:00
  • LO1: Define key concepts and terminology in machine learning.
    00:00
  • LO2: Explain different types of machine learning paradigms.
    00:00
  • LO3: Analyze real-world problems to determine suitable machine learning approaches.
    00:00
  • Multiple choice Questions
  • True/False Questions
  • Scenario Based Multiple Choice Questions
  • Key Term and Concepts Questions
  • Short Answer Questions
  • Written Assignment
  • Presentation Task
  • Role Playing Activity
  • Peer Review Task
  • Exercises and Activities Adaptation
  • Week 2: Linear Regression
  • LO1: Describe the mathematical foundations of linear regression.
    00:00
  • LO2: Apply linear regression to model relationships between variables.
    00:00
  • LO3: Evaluate model performance using appropriate error metrics.
    00:00
  • Multiple Choice Questions
  • True/False Questions
  • Scenario-Based Multiple-Choice Questions
  • Key Terms and Concepts Questions
  • Short Answer Questions
  • Written Assignment
  • Presentation Task
  • Role-Playing Activity
  • Peer Review Task
  • Exercises and Activities Adaptation
  • Week 3: Classification Algorithms
  • LO1: Identify major classification algorithms and their characteristics.
    00:00
  • LO2: Compare classification models based on performance metrics.
    00:00
  • LO3: Implement classification algorithms for predictive tasks.
    00:00
  • Multiple Choice Questions
  • True/False Questions
  • Scenario-Based Multiple-Choice Questions
  • Key Terms and Concepts Questions
  • Short Answer Questions
  • Written Assignment
  • Presentation Task
  • Role-Playing Activity
  • Peer Review Task
  • Exercises and Activities Adaptation
  • Week 4: Model Evaluation and Validation
  • LO1: Describe model evaluation techniques and validation methods.
    00:00
  • LO2: Analyze model performance using cross-validation and metrics.
    00:00
  • LO3: Assess model reliability and generalization capability.
    00:00
  • Multiple Choice Questions
  • True/False Questions
  • Key Terms and Concepts Questions
  • Scenario-Based Multiple-Choice Questions
  • Short Answer Questions
  • Written Assignment
  • Presentation Task
  • Role-Playing Activity
  • Peer Review Task
  • Exercises and Activities Adaptation
  • Week 5: Overfitting, Underfitting, and Regularization
    00:00
  • LO1: Explain the concepts of overfitting and underfitting.
    00:00
  • LO2: Analyze the impact of model complexity on performance.
    00:00
  • LO3: Apply regularization techniques to improve model generalization.
    00:00
  • Multiple Choice Questions
  • True/False Questions
  • Key Terms and Concepts Questions
  • Scenario-Based Multiple-Choice Questions
  • Short Answer Questions
  • Written Assignment
  • Presentation Task
  • Role-Playing Activity
  • Peer Review Task
  • Exercises and Activities Adaptation
  • Week 6: Clustering and Unsupervised Learning
    00:00
  • LO1: Describe clustering algorithms and unsupervised learning concepts.
    00:00
  • LO2: Apply clustering techniques to identify patterns in datasets.
    00:00
  • LO3: Evaluate clustering results using appropriate validation measures.
    00:00
  • Multiple Choice Questions
  • True/False Questions
  • Scenario Based Multiple Choice Questions
  • Key Term and Concepts Questions
  • Short Answer Questions
  • Written Assignment
  • Presentation Task
  • Role-Playing Activity
  • Peer Review Task
  • Exercises and Activities Adaptation
  • Week 7: Dimensionality Reduction
    00:00
  • LO1: Explain the need for dimensionality reduction techniques.
    00:00
  • LO2: Apply dimensionality reduction methods to high-dimensional data.
    00:00
  • LO3: Analyze the trade-offs between dimensionality reduction and information retention.
    00:00
  • Multiple choice Questions
  • True/False Questions
  • Key Term and Concepts Questions
  • Scenario Based Multiple Choice Questions
  • Short Answer Questions
  • Written Assignment
  • Presentation Task
  • Role-Playing Activity
  • Peer Review Task
  • Exercises and Activities Adaptation
  • Week 8: Midterm Test or Assignment
    00:00
  • Multiple choice Questions
  • True/False Questions
  • Scenario Based Multiple Choice Questions
  • Short Answer Questions
  • Week 9: Neural Networks and Deep Learning
    00:00
  • LO1: Describe the architecture and components of neural networks.
    00:00
  • LO2: Apply deep learning models to solve complex tasks.
    00:00
  • LO3: Analyze training processes and optimization techniques.
    00:00
  • Multiple choice Questions
  • True/False Questions
  • Key Term and Concepts Questions
  • Scenario Based Multiple Choice Questions
  • Short Answer Questions
  • Written Assignment
  • Presentation Task
  • Role-Playing Activity
  • Peer Review Task
  • Exercises and Activities Adaptation
  • Week 10: Ensemble Methods
    00:00
  • LO1: Explain the principles of ensemble learning techniques.
    00:00
  • LO2: Compare different ensemble algorithms such as bagging and boosting.
    00:00
  • LO3: Implement ensemble models to improve predictive accuracy.
    00:00
  • Multiple choice Questions
  • True/False Questions
  • Key Term and Concepts Questions
  • Scenario Based Multiple Choice Questions
  • Short Answer Questions
  • Written Assignment
  • Presentation Task
  • Role-Playing Activity
  • Peer Review Task
  • Exercises and Activities Adaptation
  • Week 11: Reinforcement Learning
    00:00
  • LO1: Define key concepts in reinforcement learning.
    00:00
  • LO2: Analyze reward structures and policy optimization methods.
    00:00
  • LO3: Design simple reinforcement learning solutions for decision-making problems.
    00:00
  • Multiple choice Questions
  • True/False Questions
  • Key Term and Concepts Questions
  • Scenario Based Multiple Choice Questions
  • Short Answer Questions
  • Written Assignment
  • Presentation Task
  • Role-Playing Activity
  • Peer Review Task
  • Exercises and Activities Adaptation
  • Week 12: Time Series Analysis
    00:00
  • LO1: Describe fundamental concepts in time series data.
    00:00
  • LO2: Apply forecasting models to temporal datasets.
    00:00
  • LO3: Evaluate forecasting performance using error metrics.
    00:00
  • Multiple choice Questions
  • True/False Questions
  • Key Term and Concepts Questions
  • Scenario Based Multiple Choice Questions
  • Short Answer Questions
  • Written Assignment
  • Presentation Task
  • Role-Playing Activity
  • Peer Review Task
  • Exercises and Activities Adaptation
  • Week 13: Natural Language Processing with Machine Learning
    00:00
  • LO1: Explain text preprocessing and feature extraction techniques.
    00:00
  • LO2: Apply machine learning algorithms to text classification tasks.
    00:00
  • LO3: Analyze model performance in natural language processing applications.
    00:00
  • Multiple choice Questions
  • True/False Questions
  • Key Term and Concepts Questions
  • Scenario Based Multiple Choice Questions
  • Short Answer Questions
  • Written Assignment
  • Presentation Task
  • Role-Playing Activity
  • Peer Review Task
  • Exercises and Activities Adaptation
  • Week 14: Applications of Machine Learning
    00:00
  • LO1: Identify real-world applications of machine learning across domains.
    00:00
  • LO2: Analyze case studies to evaluate machine learning solutions.
    00:00
  • LO3: Design machine learning approaches for practical applications.
    00:00
  • Multiple choice Questions
  • True/False Questions
  • Key Term and Concepts Questions
  • Scenario Based Multiple Choice Questions
  • Short Answer Questions
  • Written Assignment
  • Presentation Task
  • Role-Playing Activity
  • Peer Review Task
  • Exercises and Activities Adaptation
  • Week 15: Ethics and Fairness in Machine Learning
  • LO1: Explain ethical concerns and bias in machine learning systems.
    00:00
  • LO2: Analyze fairness metrics and mitigation strategies.
    00:00
  • LO3: Evaluate machine learning models for ethical compliance and responsible deployment.
    00:00
  • Multiple choice Questions
  • True/False Questions
  • Key Term and Concepts Questions
  • Scenario Based Multiple Choice Questions
  • Written Assignment
  • Presentation Task
  • Role-Playing Activity
  • Peer Review Task
  • Exercises and Activities Adaptation
  • Week 16: Final Test Or Project
  • Multiple choice Questions
  • True/False Questions
  • Scenario Based Multiple Choice Questions
  • Short Answer Questions
  • Written Assignment
  • Presentation Task
  • Role-Playing Activity
  • Peer Review Task
  • Exercises and Activities Adaptation
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