Learning Objectives
The assignment assists students in achieving the following learning objectives. For each objective, related course readings are cited.
- Define unsupervised learning; distinguish from supervised learning (e.g., classification)
[WUL, ISL 10.1, ESL 14.1, AIMA 18.1] - Define and implement k-means clustering algorithm
[WKMC 3.1, ISL 10.3.1, ESL 14.3.6, ItML 7.3] - Understand the limitations of k-means clustering
[OMP]- what are the assumptions, where does the method fail
- Awareness of practical application of the method:
Optional Learning Objectives
- Generalize to non-Euclidean, non-quantitative (e.g., k-medoids)
[WKM, ESL 14.3.10] - Relate to EM algorithm
[ESL 14.3.7, AIMA 20.3] - Common applications
[WKMC 5]
ACM/IEEE Computer Science CS2013 Curricula
The learning objectives of the assignment can be tied to the following subtopics of the ACM/IEEE Computer Science Curricula CS2013.
Topics Areas:
- IS/Advanced Machine Learning (p. 127, in PDF p. 130)
- Definition and example of broad variety of machine learning tasks
- Unsupervised Learning and clustering
- EM
- k-means
- Self-organizing maps
Relevant CS2013 Learning Outcomes:
(bold emphasis added to relate to this assignment; italics indicates partial relevance)
- Explain the the differences among the three main styles of learning: supervised, reinforcement, and unsupervised. [Familiarity]
- Implement simple algorithms for supervised learning, reinforcement learning, and unsupervised learning. [Usage]
- Determine which of the three learning styles is appropriate to a particular problem domain. [Usage]
- Evaluate the performance of a simple learning system on a real-world dataset. [Assessment]
- Characterize the state of the art in learning theory, including its achievements and its shortcomings. [Familiarity]
- Explain the problem of overfitting, along with techniques for detecting and managing the problem. [Usage]