COM SCI 260
Machine Learning Algorithms
Description: Lecture, four hours; discussion, two hours; outside study, six hours. Recommended requisite: course 180. Problems of identifying patterns in data. Machine learning allows computers to learn potentially complex patterns from data and to make decisions based on these patterns. Introduction to fundamentals of this discipline to provide both conceptual grounding and practical experience with several learning algorithms. Techniques and examples used in areas such as healthcare, financial systems, commerce, and social networking. Letter grading.
Units: 4.0
Units: 4.0
1 of 1
Most Helpful Review
Spring 2020 - The professor is truly knowledgeable on the theory of machine learning. The first part of the class, regarding the theory and the proof is interesting, where he successfully made the rigorous mathematical proof easy to follow and enhanced our understanding of Machine Learning. The second part is more practical comparing to the first half of the class.
Spring 2020 - The professor is truly knowledgeable on the theory of machine learning. The first part of the class, regarding the theory and the proof is interesting, where he successfully made the rigorous mathematical proof easy to follow and enhanced our understanding of Machine Learning. The second part is more practical comparing to the first half of the class.
AD
1 of 1
