CS 57600 - Machine Learning

Machine Learning is concerned with computer programs that “automatically” improve their performance through experience (based on data). As an introductory course to machine learning, the course introduces the fundamentals of modern machine learning. It will give a broad overview of many concepts and algorithms in machine learning, ranging from supervised learning to unsupervised learning. Topics include decision tree learning, instance-based learning, perceptron and linear modeling, probabilistic modeling, neural networks, support vector machines, ensemble learning, learning theory, and unsupervised learning with clustering. This course will provide a combination of theoretical knowledge and practical, hands-on experience in solving real-world problems through the application of machine learning.

Preparation for Course
P: Basic computer science concepts, data structure, algorithm, programming experience, knowledge of linear algebra, basic statistics, and probability is required.

Cr. 3.
Hours
Class 3.
Notes
Undergraduate registration requires department approval.
Student Learning Outcomes
1. Recognize the fundamental issues of learning problems and challenges of machine learning.
2. Compare and contrast different paradigms for learning (supervised, unsupervised, etc.).
3. Characterize the theory and key algorithms used in machine learning.
4. Formulate learning tasks to their problems, apply machine learning methods to the tasks, and evaluate the model performance.
5. Integrate multiple facets of practical machine learning for problem solving: data preprocessing, learning, regularization, and model selection.