| Code |
Name |
Description |
|
074137200
075045000
|
MACHINE LEARNING
|
This course will give an undergraduate-level introduction to machine learning providing the foundations for mathematical models and algorithms required for machine learning tasks and their applications. Topics will include supervised learning, unsupervised learning, deep learning, and reinforcement learning. This course will put an emphasis on practical applications of machine learning in artificial intelligence such as computer vision, data mining, speech recognition, text processing and bioinformatics. |
| 074137500 |
DEEP LEARNING
|
This course will introduce students to the concept of Deep learning and it will help students to understand its key principles. The course covers feed-forward neural networks, convolutional neural networks, recurrent neural network, sequence modelling, deep reinforcement learning, and other fundamental concepts and techniques. This course will also teach the students the key computations underlying deep learning. It is expected by the end of the course, students will be able to build, train and apply fully connected deep neural networks, and to know how to implement efficient neural networks using the most popular libraries for Deep Learning such as Keras, PyTorch; and Tensorflow. The course will introduce students also to a wide spectrum of deep learning applications in real-word problems. |
| 074144400 |
DATA VISUALIZATION
|
This course introduces how to design and create data graphs based on the available data and the tasks to be achieved. Topics include data modelling, data manipulation, data exploration, linking data properties to graph properties, and dashboard development. Emphasis is placed on identifying patterns, trends, and differences in data across categories, space, and time. The student will learn to evaluate the effectiveness of representative graphics, and to think critically about each design decision, such as choosing colour and choosing visual coding |
