COSC440-23S2 (C) Semester Two 2023

Deep Learning

15 points

Details:
Start Date: Monday, 17 July 2023
End Date: Sunday, 12 November 2023
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 30 July 2023
  • Without academic penalty (including no fee refund): Sunday, 1 October 2023

Description

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
  • Analyze 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 (2021 S2 domain: astrophysics)

Prerequisites

(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.

Timetable 2023

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Tuesday 09:00 - 11:00 Jack Erskine 315
17 Jul - 27 Aug
11 Sep - 22 Oct
Computer Lab A
Activity Day Time Location Weeks
01 Friday 10:00 - 11:00 Rehua 008 Computer Lab
17 Jul - 27 Aug
11 Sep - 22 Oct

Timetable Note

Please note that the course activity times advertised here are currently in draft form, to be finalised on Monday 30 January 2023 for S1 and whole year courses, and Monday 26 June 2023 for S2 courses.
Please hold off enquiries about these times until those finalisation dates.

Course Coordinator

James Atlas

Notes

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

Additional Course Outline Information

Syllabus

• 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 $1,079.00

* 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

  • COSC440-23S2 (C) Semester Two 2023