Course overview
This course provides a foundational understanding of machine learning, covering its core concepts, mathematical foundations, and diverse applications. By the end of the course, learners will be able to understand and implement basic machine learning algorithms and evaluate the algorithms on a variety of datasets using suitable evaluation metrics. This course prepares students to effectively apply machine learning techniques to real-world problems and sets the foundation for more advanced studies in machine learning and artificial intelligence.
- Supervised Learning
- Unsupervised Learning
- Neural Network
Course learning outcomes
- Describe key concepts in machine learning and their advantages and disadvantages in various applications
- Demonstrate the principles of simple machine learning algorithms and models including Linear Regression, Naive Bayes, SVM, K-Nearest Neighbour and Decision Trees
- Evaluate simple machine learning algorithms on both synthetic and real-world datasets using appropriate evaluation metrics
- Apply data preparation techniques to clean, preprocess, and transform data for machine learning tasks
Degree list
The following degrees include this course