Advanced Machine Learning: Deep Learning



Course Reference No: CRS-N-0051855 (Synchronous e-Learning)

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Course Objectives

The application of machine learning is deepening and widening in the world of business and technology. Among different machine learning algorithms, deep learning stands out. Since its introduction in 2006, deep learning has fulfilled some of the oldest artificial intelligence promises, such as autonomous/driverless vehicles, machine translation, precise and speaker-independent speech recognition, and robust visual object recognition. Deep learning systems have even beaten the human experts in their field and achieved remarkable performances. 

In this course, we will address the what, why and how of deep learning. What is deep learning? Why do we need deep learning? And how do we apply and harness the benefits of deep learning in business cases? We will focus on the three popular deep learning algorithms, namely Convolutional Neural Networks (CNN), Long/Short Term Memories (LSTM), and Generative Adversarial Networks (GAN), and their applications. The methods and platforms for implementation and evaluation of deep learning systems would be discussed. Furthermore, learners will practise employing deep learning to deal with a few applied examples using Python and Octave environments.

At the end of the course, participants will be able to:

  • Articulate an efficient procedure of implementation and evaluation of deep learning models.
  • Understand the basic definitions and applications of CNN, LSTM, and GAN.
  • Define the business case for a deep learning approach.
  • Demonstrate an understanding of the practical aspects of deep learning, its platforms and tools.
  • Solve authentic business problems with deep learning models.

Outline

  • Getting familiar with the course
  • A review on AI and Machine Learning
  • Deep Learning basic terms and definitions
  • Deep Learning applications
  • Deep Learning models
  • Convolutional Neural Networks
  • Long/Short Term Memories
  • Generative Adversarial Networks
  • Deep Learning platforms and environments
  • Python and TensorFlow example
  • Octave example
  • Solving authentic business problems with Deep Learning models
  • Deep Learning models performance measurement and evaluation

Tools

Python, Octave

Pre-requisites

Basic knowledge of machine learning and Python programming

Who Should Attend

IT Engineers, IT Consultants, IT Managers, Technology Managers, Business Managers

Mode of Training

Online (Live)

Speaker

Dr. Amirhassan Monajemi 

 

Amirhassan Monajemi is a Senior Lecturer in AI and Data Science with the School of Continuing and Lifelong Education (SCALE) at the National University of Singapore (NUS). Before joining the NUS, he was with the Faculty of Computer Engineering, University of Isfahan, Iran, where he was serving as a professor of AI, Machine Learning, and Data Science. He was born in 1968 in Isfahan, Iran. He studied towards BSc and MSc in Computer Engineering at Isfahan University of Technology (IUT), and Shiraz University respectively. He got his PhD in computer engineering, pattern recognition and image processing, from the University of Bristol, Bristol, England, in 2005. His research interests include AI, Machine Learning, Machine Vision, IoT, Data Science, and their applications.

He has taught the artificial intelligence courses, including AI, Advanced AI, Expert Systems, Decision Support Systems, Neural Networks, and Cognitive Science since 2005 at both undergraduate and postgraduate levels. He was awarded the best university teacher of the province in 2012. He also has studied Learning Management Systems, E-Learning, and E-Learning for workplaces since 2007.

Dr. Monajemi has registered a few patents in the fields of AI, Machine Vision, and Signal Processing applications, including an AI and machine vision-based driver drowsiness detection system and a low power consuming spherical robot. He also has published more than a hundred research papers in peer-reviewed, indexed journals and international conferences (IEEE, Elsevier, Springer, and so on), and supervised several Data Science, IoT, and AI industrial projects in various scales, including Isfahan intelligent traffic system delivery and testing, and red light runners detection. He is experienced in different sub-domains of Artificial Intelligence and Machine Learning, from theory to practice, including Deep Learning, Logic, and Optimization.

DATE

14 Dec 2020 (Online)

DURATION

1 Day
9.00am to 5.30pm 

VENUE

National University of Singapore
University Town

International Participants

S$1,123.50

Incl. GST

Singapore Citizens (39 yrs old or younger) or Singapore PRs

S$337.05

Incl. GST

Singapore Citizens (40 yrs or older)

S$127.05

Incl. GST

Enhanced Training Support for SMEs

S$127.05

Incl. GST

Fees & Fundings

International
Participant
Singapore Citizen1
39 years old or younger
Singapore Citizen1
40 years or older eligible for MCES2
Singapore PRs Enhanced Training
Support for SMEs3
Full Programme Fee S$1,050.00 S$1,050.00 S$1,050.00 S$1,050.00 S$1,050.00
SkillsFuture Funding
(Refer to Funding Page for Claim Period)
- (S$735.00) (S$735.00) (S$735.00) (S$735.00)
Nett Programme Fee S$1,050.00 S$315.00 S$315.00 S$315.00 S$315.00
7% GST on Nett
Programme Fee
S$73.50 S$22.05 S$22.05 S$22.05 S$22.05
Total Nett Programme
Fee Payable, Incl. GST
S$1,123.50 S$337.05 S$337.05 S$337.05 S$337.05
Less Additional Funding if
Eligible Under Various Scheme
- - (S$210.00) - (S$210.00)
Total Nett Programme Fee, Incl. GST,
after additional funding from the various funding schemes
S$1,123.50 S$337.05 S$127.05 S$337.05
S$127.05


  1. All self-sponsored Singaporeans aged 25 and above can use their $500 SkillsFuture Credit to pay for the programme. Visit http://www.skillsfuture.sg/credit to select the programme. 
  2. Mid-Career Enhanced Subsidy (MCES) - Singaporeans aged 40 and above may enjoy subsidies up to 90% of the programme fee. 
  3. Enhanced Training Support for SMEs (ETSS) - SME-sponsored employees (Singaporean Citizens and PRs) may enjoy subsidies up to 90% of the programme fee. For more information, visit http://www.ssg.gov.sg/programmes-and-initiatives/training/enhanced-training-support-for-smes.html?_ga=2.154478072.1748789781.1519700056-512306731.1519700056
  4. Eligible organisations (excluding government entities) may apply for the absentee payroll funding via SkillsConnect at www.skillsconnect.gov.sg for Singaporean/permanent resident participants attending the programme during working hours. The absentee payroll funding is computed at 80% of hourly basic salary capped at $4.50 per hour or $7.50 per hour for SME. For more information, visit https://www.skillsconnect.gov.sg/sop/portal/e-Services/For%20Employers/AbsenteePayroll.jsp
  5. Eligible individuals may apply for training allowance capped at $6/hr under the WSS scheme, visit- https://www.wsg.gov.sg/programmes-and-initiatives/workfare-skills-support-scheme-individuals.html for more information on WSS.
  6. NTUC Training Fund (SEPs) – All self-employed (i.e. freelancers and sole-proprietors-with-no-employee) Singaporeans and Permanent Residents are eligible to apply for the NTUC Training Fund (SEPs) from NTUC’s Employment and Employability Institute (e2i). Click here for more information.
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18 November 2020