|
| 1.
| [1]
understand the motivations and principles for building adaptive systems based on empirical data, and how machine learning relates to the broader field of artificial intelligence |
| 2.
| [2]
formulate problems associated with domain specific data (e.g., image classification, document clustering) in terms of abstract models of machine learning |
| 3.
| [3]
implement solutions to machine learning problems using tools such as Matlab or Octave, apply numerical optimization algorithms |
Mapping from Course Learning Outcomes to Programme Learning Outcomes
| | PLO a | PLO b | PLO c | PLO d | PLO e | PLO f | PLO g | PLO h | PLO i | PLO j |
| CLO 1 | T | | | | | | | | | T |
| CLO 2 | T,P | | T,P | | | | | | | |
| CLO 3 | P | | P | | | | | | | |
T - Teach, P - Practice
For BEng(CompSc) Programme Learning Outcomes, please refer to
here.
|
| Syllabus |
|
Calendar Entry:
This course introduces algorithms, tools, practices, and applications of machine learning. Topics include core methods such as supervised learning (classification and regression), unsupervised learning (clustering, principal component analysis), Bayesian estimation, neural networks; common practices in data pre-processing, hyper-parameter tuning, and model evaluation; tools/libraries/APIs such as scikit-learn, Theano/Keras, and multi/many-core CPU/GPU programming.
|
|
Detailed Description:
| Introduction |
Mapped to CLOs
|
| Principles of data-driven systems and AI | 1 |
| Decision theory | 1, 2 |
| Supervised learning |
Mapped to CLOs
|
| Dataset assessment and pre-processing | 2, 3 |
| Linear classifiers | 2, 3 |
| Logistic regression | 2, 3 |
| Neural networks | 2, 3 |
| Performance evaluation and tuning | 2, 3 |
| Unsupervised learning |
Mapped to CLOs
|
| Clustering | 2, 3 |
| Mixture models | 2, 3 |
| Principal components analysis | 2, 3 |
| Advanced topics and applications |
Mapped to CLOs
|
| Applications | 1, 2 |
|
|
Assessment:
Continuous Assessment:
50%
Written Examination:
50%
|
| Teaching Plan |
|
Please refer to the corresponding Moodle course.
|
| Moodle Course(s) |
|
|
