COSC440-24S2 (C) Semester Two 2024

Deep Learning

15 points

Start Date: Monday, 15 July 2024
End Date: Sunday, 10 November 2024
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 28 July 2024
  • Without academic penalty (including no fee refund): Sunday, 29 September 2024


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.

Covid-19 Update: Please refer to the course page on AKO | Learn for all information about your course, including lectures, labs, tutorials and assessments.

Learning Outcomes

  • Understand the fundamental algorithms of deep learning systems such as backpropagation
  • Show competency in programming deep learning models
  • Know 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 learning
  • Analyse advanced network designs in major problem domains such as generative models for creating synthesized (fake) text, images, video, and audio
  • Refine and engineer models for domain constraints such as performance in edge systems
  • Research, propose, and implement a project involving state of the art deep learning networks for a given application domain (2023 S2 domain: protein folding)


(1) 30 points of 300-level COSC/SENG/DATA/ENCE/ENEL301; and (2) COSC122; and (3) COSC262 or ENEL300 or ENMT301

Timetable 2024

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Thursday 15:00 - 17:00 Rata 222 & 223 Drawing Office
15 Jul - 25 Aug
9 Sep - 20 Oct
Computer Lab A
Activity Day Time Location Weeks
01 Thursday 17:00 - 18:00 Jack Erskine 133 Lab 2
15 Jul - 25 Aug
9 Sep - 20 Oct
02 Tuesday 15:00 - 16:00 Jack Erskine 133 Lab 2
15 Jul - 25 Aug
9 Sep - 20 Oct

Timetable Note

Please note that the course activity times advertised here are currently in draft form, to be finalised at the end of January for S1 and whole year courses, and at the end of June for S2 courses.

Please hold off enquiries about these times until those finalisation dates.

Course Coordinator

James Atlas

Additional Course Outline Information


• 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

Indicative Fees

Domestic fee $1,110.00

* 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 .

All COSC440 Occurrences

  • COSC440-24S2 (C) Semester Two 2024