|
| 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:
An introduction to algorithms and applications of machine learning. Topics include: decision theory; parametric models; supervised learning (classification and regression); unsupervised learning (clustering, mixture models, principal component analysis); Bayesian methods.
|
|
Detailed Description:
| Introduction |
Mapped to CLOs
|
| Principles of data-driven systems and AI | 1 |
| Curve fitting | 1, 2 |
| Decision theory | 1, 2 |
| Supervised learning |
Mapped to CLOs
|
| Linear classifiers | 2, 3 |
| Logistic regression | 2, 3 |
| Neural networks | 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
|
| Gaussian processes | 2, 3 |
| Applications | 1, 2 |
|
|
Assessment:
Continuous Assessment:
50%
Written Examination:
50%
|
| Teaching Plan |
|
Please refer to the corresponding Moodle course.
|
| Moodle Course(s) |
|
|
