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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.
The description text above is for a previous occurrence. For 2020 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 (2020 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 (2020 S2 domain: astrophysics)
(1) COSC480 orequivalent and at least 30 points at 200-300 level MATH/EMTH/STAT; (2) Subject to approval of the Head of Department of the Computer Science and Software Engineering.
Students must attend one activity from each section.
The prerequisites listed on this page are for a previous occurrence. For 2020, 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 conceptsTypes of learning (supervised, self-supervised, reinforcement) Types of problems (classification, regression)Loss functions, gradient descent and optimizationAutomatic differentiation, forward and backward modeDiagnosing problems: under and over-fitting, regularization, initialization Deep learning conceptsMulti-dimensional arrays and memory models, views and vectorized operationsNeural networks: perceptron, layers, types of operations (linear, convolutional, pooling, sampling, nonlinearity), visualizationSequential and recurrent networksTransfer learning, synthesis, ensemble networksDeep learning problems, models, and researchComputer Graphics and Vision object detection, segmentation, image retrieval, face reidentificationfeature pyramid networks, similarity learning, discriminatory networks, adversarial networks, generative networksNatural languageparsing, window prediction, generation, translationencoders/decoders, latent space, autoencoders transformers, long short-term memory, attentionAudio and Video SynthesisText-to-speech, music generation, deep fakes, semantic modelsTime series forecasting, autoregression, dilated convolution, few-shot learningSearch using deep reinforcement learningMolecular design, game playingneural network architecture design, compression, quantizationAgents, Markov decision processes, Monte Carlo, policy gradient methodsAnomaly detectionIntrusion detection, fraud detection, scientific discoveryOne-class neural networks, zero shot learningIrregular networksRecommender systems, molecular structure and property predictionGraph convolutional networks, point cloud processing, spatial-temporal networks
Domestic fee $1,022.00
International fee $4,800.00
* Fees include New Zealand GST and do not include any programme level discount or additional course related expenses.
For further information see
Computer Science and Software Engineering.