Podcast
Questions and Answers
What is the time allocated for the Case study on APGAR score?
What is the time allocated for the Case study on APGAR score?
- 15
- 10 (correct)
- 25
- 20
Which activity has the longest allocated time?
Which activity has the longest allocated time?
- Analysis Sitting rising test
- Discussion Learning
- Demo Computer evolution (correct)
- Challenge Machine learning
According to the Oxford English Dictionary, what is the definition of learning in the context of psychology?
According to the Oxford English Dictionary, what is the definition of learning in the context of psychology?
- The modification of behavior due to natural development
- The acquisition of new abilities through growth
- The process of maturation leading to new responses
- The action of receiving instruction or acquiring knowledge (correct)
Which of the following is NOT listed as an application of Machine Learning?
Which of the following is NOT listed as an application of Machine Learning?
Where is the application of Spam detection mentioned in the context of Machine Learning?
Where is the application of Spam detection mentioned in the context of Machine Learning?
What is the time allocated for the Discussion activity?
What is the time allocated for the Discussion activity?
Which activity has the same allocated time as the Challenge activity?
Which activity has the same allocated time as the Challenge activity?
In the Machine Learning context, which application is NOT mentioned?
In the Machine Learning context, which application is NOT mentioned?
What is the total time allocated for the activities related to Analysis?
What is the total time allocated for the activities related to Analysis?
Where is the application of Customer segmentation mentioned in the context of Machine Learning?
Where is the application of Customer segmentation mentioned in the context of Machine Learning?
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Study Notes
Course Overview
- Machine Learning course (18-785) led by Patrick McSharry at Carnegie Mellon University.
- Focuses on applying machine learning techniques to large real-world datasets for knowledge discovery, predictive analytics, and decision support.
Learning Objectives
- Develop expertise in machine learning methodologies and their applications.
- Introduce and demonstrate techniques for data refining, visualization, exploration, and modeling.
- Analyze advantages and disadvantages of various machine learning methods including linear, nonlinear, nonparametric, and ensemble approaches.
- Address challenges in both supervised and unsupervised learning contexts.
Textbooks
- The Elements of Statistical Learning by T. Hastie, R. Tibshirani, and J. Friedman (Springer-Verlag, 2001).
- Pattern Recognition and Machine Learning by Christopher Bishop (Springer, 2006).
Weekly Course Outline
- Week 7: Statistical learning.
- Week 8: Linear models.
- Week 9: Nonlinear models.
- Week 10: Supervised learning.
- Week 11: Unsupervised learning.
- Week 12: Ensemble approaches.
Instructor Contact Information
- Patrick McSharry
- Email: [email protected]
- Website: www.mcsharry.net
- Twitter: @patrickmcsharry
- Course occurs in Fall 2023 at ICT Center of Excellence.
Important Concepts
- Statistical learning encompasses both traditional statistical methods and modern machine learning techniques.
- Understanding different modeling approaches is crucial for effective application of machine learning in varied contexts.
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