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Course Detail

CZ4042 Neutral Networks and Deep Learning
This course covers the information processing techniques inspired by the workings of biological neural networks, which provides solution to interrogatives that current linear systems are not able to resolve. With insights into the Biological neuron, knowledge on the diverse artificial neural networks, the student will be able to design and select suitable artificial neural network model for solving real world applications, and perform the required simulations and implementations.
On successful completion of this course, students will have:
a) Understand that current artificial neurons are simple abstractions of the biological neurons, realized as elements in a program or as circuits made of silicon.
b) Recognize that current artificial neural networks, although yet to come close to having a fraction of the power of the human brain, can be trained to perform useful functions.
c) Possess a good idea on diverse forms of artificial neural networks.
d) Understand the underlying principles, structures and operations of different artificial neural networks.
e) Supervised and Unsupervised learning. Design and select a suitable artificial neural network model for an application.
f) Understand what artificial neural network can do for you. Appreciate successful applications in the areas of pattern classification, function approximation and time series prediction.
Who Should Attend
Working professionals with prior knowledge of:

Pre-requisite: CZ1007 Data Structures, CZ1012 Engineering Mathematics II
Eligibility Criteria
Participants who have completed the Specialist Certificate in Software Basics II OR relevant Bachelor degree holder and Polytechnics Diploma with relevant work experiences.
Date(s): 09 Aug 2021 to 03 Dec 2021
Time: Refer to Class and Exam Schedules
Venue: TR+15
Closing Date of Registration: 30 Nov 2020
Course Fee Payable:(Inclusive of GST) Refer to the course fee table

E2I No
Academic Units (AU)
Number of AU: 3
Online Registration
Method of Payment
  1. Online Credit/Debit Card Payment (VISA and Mastercard only)
  2. Cash/Cheque/NETS payment at One-Stop@SAC (NTU Main Campus)
Withdrawal & Refund Policy

Once payment is made, applicant is committed to the completion of course. Course fee refunds will not be considered.

Terms and Conditions
  1. Course is subject to a minimum participation number before commencement.
  2. Course is subject to a first-come-first-serve basis.
  3. Registration is non-transferable.
  4. Student must meet all eligibility criteria for admission.
  5. Student is required to complete all assessments for each course.
  6. PaCE@NTU​ reserves the right to change or cancel any course or lecturer due to unforeseen circumstances.
  7. All details are correct at time of dissemination.
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