Advance Machine Learning for Telecommunications
Course Code
ET4243
Number of Credits
3
Semester
Course Type
Study Material
| Study Material | Depth |
|---|---|
| Simulation and Modeling | Expert |
| Reinforcement Learning | Expert |
| Deep Learning. | Expert |
| Verbal Communication | |
| Engineering Design | Expert |
Graduate Learning Outcomes (GLO) carried by the course
| CPMK Code | Course Learning Outcomes Elements (CLO) |
|---|---|
| CPMK 1 | Ability to apply scientific and mathematical relationships (principles or laws) and necessary inputs to given problems in simulation and modeling, reinforcement learning, and deep learning. |
| CPMK 2 | Ability to analyze problems and identify opportunities to produce design problem statements related to simulation and modeling, reinforcement learning, and deep learning. |
| CPMK 3 | Ability to identify constraints to generate design requirements related to simulation and modeling, reinforcement learning, and deep learning. |
| CPMK 4 | Ability to identify and formulate engineering problems related to simulation and modeling, reinforcement learning, and deep learning. |
| CPMK 5 | Ability to analyze and solve engineering problems related to simulation and modeling, reinforcement learning, and deep learning. |
| CPMK 6 | Ability to apply the use of modern engineering tools and integrate in engineering projects related to simulation and modeling, reinforcement learning, and deep learning. |
| CPMK 7 | Ability to prepare and present technical presentations orally through various media related to simulation and modeling, reinforcement learning, and deep learning. |
| CPMK 8 | Ability to collect information about new knowledge through available media related to simulation and modeling, reinforcement learning, and deep learning. |
| CPMK 9 | Ability to incorporate new knowledge into engineering work related to simulation and modeling, reinforcement learning, and deep learning. |
Learning Method
- Lecture Group discussion Problem/Case Study based learning
Learning Modality
- Synchronous Offline Asynchronous Online Mix
Assessment Methods
- Quizzes, Mid Semester Exam, Final Exam