Course Description
This course introduces data mining and AI/machine learning concepts, techniques, and performance evaluation. This course discusses data characterization, data preprocessing, classification, and regression. This course covers classical classification techniques, such as rules-based methods, decision tree, nearest neighbor, probabilistic methods, and discriminant functions. This course exposes students to deep learning, including multilayer perceptron, learning algorithms, and advanced neural network architecture. This course will also discuss classical AI, expert systems, generative AI, and classic AI algorithms (search, optimization, rule-based systems). Students will get a foundational understanding of data mining and AI/machine learning and hands-on skills in data mining and machine learning.
Course Outcomes
After completing the course, students should be able to:
1. Identify data measurement scale types and convert between scale types.
2. Demonstrate and apply data preprocessing methods.
3. Demonstrate and apply classification techniques.
4. Demonstrate and apply regression techniques.
5. Demonstrate and apply deep learning techniques.
6. Evaluate the performance of data mining and AI/machine learning models.
7. Discuss applications of Artificial Intelligence techniques in cybersecurity and related domains
8. Identify solutions to business problems by utilizing intelligent systems and machine learning.
