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This course introduces students to theoretical foundational concepts of deep neural networks. The focus of this course is on both fundamental theoretical and applied methods in deep neural networks. A range of topics from convolutional and recurrent type networks to neural-network generative models and attention mechanisms will be introduced.
2021 Covid-19 Update: Please refer to the course page on AKO | Learn for all information about your course, including lectures, labs, tutorials and assessments.The description text above is for a previous occurrence. For 2021 this course introduces students to the core concepts of deep neural networks. The course focuses on the computational process of problem formulation, model selection and design, implementation, analysis, and refinement for deep neural networks. We analyze a range of advanced neural network designs with transformative results in computer vision, natural language, anomaly detection, molecular design, and deep fakes. Students build competency in the theory and practice of creating deep neural network applications and will research, propose, and implement their own deep learning network for a given application domain (2021 S2 domain: astrophysics).
Understand the fundamental algorithms of deep learning systems such as backpropagationShow competency in programming deep learning modelsKnow which model architectures to use for processing different types of data (images, sequences, and graphs)Train neural networks through supervised learning, unsupervised learning, and reinforcement learningAnalyze advanced network designs in major problem domains such as generative models for creating synthesized (fake) text, images, video, and audioRefine and engineer models for domain constraints such as performance in edge systemsResearch, propose, and implement a project involving state of the art deep learning networks for a given application domain (2021 S2 domain: astrophysics)
(1) COSC262; (2) 30 points of 300-level COSC/SENG/DATA; (3) Approval by the Head of the Department of Computer Science and Software Engineering.
2021 Covid-19 Update: Please refer to the course page on AKO | Learn for all information about your course, including lectures, labs, tutorials and assessments.
The prerequisites listed on this page are for a previous occurrence. For 2021, students can be approved into this course who have passed at least: (1) COSC262 and (2) 30 points of 300-level COSC/SENG/DATA
• Machine learning concepts• Types of learning (supervised, self-supervised, reinforcement) • Types of problems (classification, regression)• Loss functions, gradient descent and optimization• Automatic differentiation, forward and backward mode• Diagnosing problems: under and over-fitting, regularization, initialization • Deep learning concepts• Multi-dimensional arrays and memory models, views and vectorized operations• Neural networks: perceptron, layers, types of operations (linear, convolutional, pooling, sampling, nonlinearity), visualization• Sequential and recurrent networks• Transfer learning, synthesis, ensemble networks• Deep learning problems, models, and research• Computer Graphics and Vision • object detection, segmentation, image retrieval, face reidentification• feature pyramid networks, similarity learning, discriminatory networks, adversarial networks, generative networks• Natural language• parsing, window prediction, generation, translation• encoders/decoders, latent space, autoencoders • transformers, long short-term memory, attention• Audio and Video Synthesis• Text-to-speech, music generation, deep fakes, semantic models• Time series forecasting, autoregression, dilated convolution, few-shot learning• Search using deep reinforcement learning• Molecular design, game playing• neural network architecture design, compression, quantization• Agents, Markov decision processes, Monte Carlo, policy gradient methods• Anomaly detection• Intrusion detection, fraud detection, scientific discovery• One-class neural networks, zero shot learning• Irregular networks• Recommender systems, molecular structure and property prediction• Graph convolutional networks, point cloud processing, spatial-temporal networks
Domestic fee $1,033.00
International Postgraduate fees
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see
Computer Science and Software Engineering