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

Week 1: Introduction to Machine Learning

  • 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

Week 3: Classification Algorithms

Week 4: Model Evaluation and Validation

Week 5: Overfitting, Underfitting, and Regularization

Week 6: Clustering and Unsupervised Learning

Week 7: Dimensionality Reduction

Week 8: Mid Term

Week 9: Neural Networks and Deep Learning

Week 10: Ensemble Methods

Week 11: Reinforcement Learning

Week 12: Time Series Analysis

Week 13: Natural Language Processing with Machine Learning

Week 14: Applications of Machine Learning

Week 15: Ethics and Fairness in Machine Learning

Week 16: Final Test

Florida Coastal University

Learning often happens in classrooms
but it doesn’t have to.

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