COVID19 Notice
Recent research suggests that talking without a mask inside poorly ventilated spaces contributes to the spreads of COVID19. Please wear masks and avoid talking during the class. In-class questions and discussions should be posted on Campuswire (class code: 6453).
Course Description
Machine learning is a sub-field of Artificial Intelligence that gives computers the ability to learn and/or act without being explicitly programmed. Applications of machine learning have permeated many aspects of every-day life and can be found among others in self-driving cars, speech recognition, computer vision, and genomics. Topics include supervised and unsupervised learning (including parametric and non-parametric models, clustering, dimension reduction, deep learning), optimization procedures, and statistical inference.
Learning Outcomes
The objective of this course is to teach fundamental methods of machine learning with focus on the theoretical underpinnings, practical implementations, and experimentation. Upon completion of the course students will:
- Have a good understanding of the fundamental issues and challenges of machine learning: data, model selection, model complexity, etc.
- Gain an understanding of the strengths and weaknesses of many popular machine learning approaches.
- Uncover the underlying mathematical relationships within and across Machine Learning algorithms and the paradigms of supervised and unsupervised learning.
- Be able to design and implement various machine learning algorithms in a range of real-world applications.
Staff
| Instructor | Guni Sharon |
| guni@tamu.edu | |
| Office Hours | Tuesday, 9:00 -- 10:00 am |
| Office | Zoom |
| TA | Sheelabhadra "Sheel" Dey |
| sheelabhadra@tamu.edu | |
| Office Hours | Friday, 12:00 pm -- 1:00 pm |
| Office | EABC 107B or Zoom |