[Pre – Learning] BSCS_CIS402: Machine Learning

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.

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.

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
Knowledge Check