Description:
Course Description: This course provides in-depth conceptual explanation of supervised and unsupervised machine learning algorithms and how to implement them to address real-world problems.
Prerequisites: None
Learning Outcome:
– Assess and understand the core principles, applications, and limitations of machine learning, distinguishing its role from traditional programming
– Analyze a variety of supervised and unsupervised machine learning algorithms using prominent frameworks
– Integrate machine learning knowledge to address real-world challenges by choosing appropriate algorithms and techniques
| Module |
Topic & Readings |
| Module 1 |
Intro to Machine Learning
Understand and Build Machine Learning Models |
| Module 2 |
Supervised Machine Learning Algorithms
Logistic and Linear Regression |
| Module 3 |
Understand Machine Learning Algorithms
Applications of Clustering |
| Module 4 |
Capstone Project |
Faculty: Jin Kocsis