INFO 251
Applied Machine Learning
Provides a theoretical and practical introduction to modern techniques in applied machine learning. Covers key concepts in supervised and unsupervised machine learning, including the design of machine learning experiments, algorithms for prediction and inference, optimization, and evaluation. Students will learn functional, procedural, and statistical programming techniques for working with real-world data.
Topics
- • Effectively design, execute, and critique experimental and non-experimental methods from statistics, machine learning, and econometrics. • Understand the difference between causal and non-causal relationships, and which situations and methods are appropriate for both forms of analysis. • Understand the principles, advantages, and disadvantages of different algorithms for supervised and unsupervised machine learning. • Implement basic algorithms on structured and unstructured data, and evaluate the performance of these algorithms on a variety of real-world datasets.
Official catalogue course objectives and student learning outcomes
Prerequisites & course information
{'preRequisiteCourse': '1107621', 'concurrentPrerequisit': False}; {'preRequisiteCourse': '1107201', 'concurrentPrerequisit': False}; {'preRequisiteCourse': '1107202', 'concurrentPrerequisit': False}
Sources
- Course catalogue ↗2026-2027