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| 1.
| [1]
understand the motivations and principles for building deep learning systems based on empirical data, and how deep learning relates to the broader field of artificial intelligence. |
| 2.
| [2]
formulate problems associated with domain specific data (e.g., image recognition, image generation, reinforcement learning, and language translation) in terms of deep learning models.
|
| 3.
| [3]
implement solutions to computer vision, natural language processing, and robotic problems using deep learning toolboxes such as PyTorch or Tensorflow, 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 | | | | | | | | T |
| CLO 2 | T | | T | T | | | | | | |
| CLO 3 | T | | | T | | | | | T | |
T - Teach, P - Practice
For BEng(CompSc) Programme Learning Outcomes, please refer to
here.
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|
Calendar Entry:
An introduction to algorithms and applications of deep learning. The course helps students get hands-on experience of building deep learning models to solve practical tasks including image recognition, image generation, reinforcement learning, and language translation. Topics include: machine learning theory; optimization in deep learning; convolutional neural networks; recurrent neural networks; generative adversarial networks; reinforcement learning; self-driving vehicle.
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Detailed Description:
| Introduction |
Mapped to CLOs
|
| Course Introduction:
Overview of computer vision, natural language processing, reinforcement learning in video game, and historical context.
Introduction to Python/NumPy.
| 1 |
| Loss Functions and Optimization:
Linear classification, higher-level representations, image features, optimization, stochastic gradient descent (SGD).
| 1, 2 |
| Introduction to Neural Networks:
Backpropagation, Multi-layer Perceptrons.
| 1, 2 |
| Case #1: Able to build neural networks with backpropagation and SGD. | 2, 3 |
| Convolutional Neural Networks |
Mapped to CLOs
|
| Convolutional Neural Networks:
History, Convolution and pooling, ConvNets beyong vision, Introduction to PyTorch
| 1, 2 |
| CNN Architectures:
AlexNet, VGG, GoogLeNet, ResNet, etc
| 1, 2 |
| Case #2: Able to finetune and evaluate deep neural networks (e.g. AlexNet) for image recognition and others. | 2, 3 |
| Generative Adversarial Networks |
Mapped to CLOs
|
| GAN Architectures:
Vanilla GAN, WGAN, DCGAN, CGAN, BEGAN, SRGAN, etc.
| 1, 2 |
| Case #3: Able to finetune and evaluate SRGAN on CelebA for face image generation, super resolution, and others | 2, 3 |
| Reinforcement Learning |
Mapped to CLOs
|
| RL basics: policy gradients, hard attention | 1, 2 |
| Q-Learning | 1, 2 |
| Actor-Critic | 1, 2 |
| Case #4: Able to train CNN_DQN to play Atari. | 2, 3 |
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