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Short Course

Advanced Machine Learning: Deep Learning

Programme ScheduleDuration (days)Mode of DeliveryProgramme Code
To be advised1Classroom LearningTGS-2021004677

Course Overview

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 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.

Course Objectives

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

  • Articulate an efficient procedure for the 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.

Who Should Attend

  • IT engineers, consultants and managers
  • Technology managers
  • Business managers

Prerequisites

Basic knowledge of machine learning and Python programming. Click to view the course detail of Python Programming.

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Types of Available Fundings

  • SkillsFuture Credit
  • UTAP
  • PSEA
  • Enhanced Training Support
  • SFEC

    • Instructors


    Dr Amirhassan Monajemi

    Dr Amirhassan Monajemi

    Dr Amirhassan Monajemi is a Senior Lecturer in AI and Machine Learning with the School of Computing (SoC) at the National University of Singapore (NUS). Prior to SoC, he was a Senior Lecturer in NUS School of Continuing and Lifelong Education (SCALE) teaching AI and Data Science to adult learners. Before joining 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 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 Optimisation.

    Dr Edmund Low

    Dr Edmund Low

    Dr Edmund Low is currently Senior Lecturer with the NUS College at the National University of Singapore. He has nearly 20 years of academic and professional experience in the use of data-driven tools to answer questions in public health and the environment. His past projects include applying AI techniques and machine learning models for environmental modelling and impact assessment. He currently heads the quantitative reasoning domain at USP, and teaches courses on statistical methods, data science and machine learning. As an educator, Edmund is a multiple recipient of both the USP Teaching Excellence Award, as well as the NUS Annual Teaching Excellence Award. Edmund holds a PhD in Environmental Engineering from Yale University.

    Programme Fees


    Singapore Citizens Singapore PRs Enhanced Training Support for SMEs International Participants
    39 years old
    or younger    		 40 years old
    or older
    Full Programme Fee S$1,050.00 S$1,050.00 S$1,050.00 S$1,050.00 S$1,050.00
    Less: SSG Grant Amount S$735.00 S$735.00 S$735.00 S$735.00 -
    Nett Programme Fee S$315.00 S$315.00 S$315.00 S$315.00 S$1,050.00
    9% GST on Nett Programme Fee S$28.35 S$28.35 S$28.35 S$28.35 S$94.50
    Total Nett Programme Fee Payable, Including GST S$343.35 S$343.35 S$343.35 S$343.35 S$1,144.50
    Less Additional Funding - S$210.00 - S$210.00 -
    Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes S$343.35 S$133.35 S$343.35 S$133.35 S$1,144.50
    1. Total Nett Programme Fee Payable, including GST, after additional funding from various funding schemes.
    2. GST shall apply at prevailing rates.
    3. Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.

    Other Information

    This course is eligible for Union Training Assistance Programme (UTAP). NTUC members can enjoy up to 50% funding (capped at $250 per year) under UTAP. NTUC members aged 40 and above can enjoy higher funding support up to $500 per individual each year, capped at 50% of unfunded course fees, for courses attended between 1 July 2020 to 31 December 2025. Please click here for more information.

    Connect with us

    11 Research Link
    COM 3, #B1-07
    Singapore 119391
    Telephone: +65 6516 5523
    Email: soc-ace@nus.edu.sg

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