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COMPUTER ENGINEERING (ENGLISH) PROGRAMME
COURSE DESCRIPTION
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| Name of the Course Unit
| Code
| Year
| Semester
| In-Class Hours (T+P)
| Credit
| ECTS Credit
|
| INTRODUCTION TO MACHINE LEARNING |
TEL419 |
4 |
8 |
3+0 |
3.0 |
6.0 |
| Objectives and Contents |
| Objectives of the Course Unit |
Machine learning is the science of getting computers to act without being explicitly programmed.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. |
| Contents of the Course Unit |
Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas. |
| Contribution of the Course Intending to Provide the Professional Education |
To learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.
Gain understanding on learning from memorization, examples, explanation, and exploration. |
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