Applied Machine Learning
Dates: 22 Jun, 23 Jun 2023 | 9am – 5.30pm | Online
Duration: 2 Days
Course Overview
This course will introduce participants to machine learning, focusing more on the practical and applied aspects rather than theory. The course will discuss machine learning concepts, and briefly introduce Python, PyCharm environment, Scikit-learn, Numpy, Anaconda, and Keras toolkits.
Regression as a basic machine learning method will be discussed and practised. Different models and examples of regression will be reviewed. Support Vector Machines (SVM) along with their applications in function estimation and classification will also be introduced. We will also discuss artificial neural networks and introduce deep learning.
This module is part of Professional Certificate in Applied Machine Learning.
Course Objectives
Participants will learn how to implement machine learning to solve real-life problems more productively and efficiently.
Learning Outcomes
At the end of this course, participants will be able to:
• Understand the way regression, support vector machines (SVM), and artificial neural networks (ANN) work
• Recognise the applications, advantages and disadvantages of regression, SVM, and ANN methods
• Design and implement basic regression, SVM-based, and ANN-based algorithms in clustering, classification, and function estimation applications
Topics
- Overview
- Getting familiar with the course
- A brief review of AI
- Machine Learnings definitions and terms
- Machine Learning applications
- Introduction to Python programming
- Regression
- Linear regression
- Non-linear regression
- How to implement regression in Python?
- SVM
- What are Support Vector Machines (SVM)?
- SVM implementation
- Artificial Neural Networks
- What are Artificial Neural Networks (ANNs)?
- History
- Basic ANN models: Perceptron
- Training
- Multilayer Perceptrons (MLP) and non-linear mapping
- Supervised and unsupervised schemes
- How to implement and train an MLP in Python?
- Generality
- Evaluation and Performance Measurement
- Deep Learning Introduction
- What is Deep Learning?
- Why Deep Learning is Important and Effective?
- State of the Art Instances
- Basic Deep Learning Models
- Deep Learning Environments
Who Should Attend
Data Analysts, IT Experts, Chief Technology Officers (CTOs), Technical Advisors, Intermediate-level Managers
Prerequisites
Basic AI knowledge and basic Python programming skills. Click to view the course detail of Python Programming.
Course Conveners
(Click their photos to view their short biographies)
Dr Ai Xin
Dr Ai Xin
Dr Ai Xin is currently a Lecturer with the School of Computing at the National University of Singapore (NUS). She has many years’ experience on teaching Artificial Intelligence and Data Science courses, e.g. machine learning, deep learning, data mining and etc.
She graduated from NUS with a PhD degree on Electrical and Computer Engineering. Her research focused on Game Theoretical Modelling, Optimization Methods, Algorithm Design and Wireless Networks.
She worked in BHP Billiton Marketing Asia for eight years and gained a lot of industry experience through different functions, e.g. risk management, supply chain management, sales and marketing planning and etc.
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 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 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.
Dr Manoranjan Dash
Dr Manoranjan Dash
Dr Manoranjan Dash is a Senior Data Scientist in Data Science Consortium at the National University of Singapore. Before joining the NUS, he was with A*Star as a senior scientist and with NTU as an assistant professor.
Dr Dash has extensive experience both in teaching machine learning topics and implementing machine learning (data science) related industry projects. He has taught machine learning and its related subjects for more than 10 years. He has also published more than 70 machine learning related technical papers in reputed conferences and journals. You can see a partial list of publications here.
Course Fees
Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes
Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.
NTUC members enjoy 50% unfunded course fee support for up to S$250 each year (or up to S$500 for NTUC members aged 40 years old and above) when you sign up for courses supported under UTAP (Union Training Assistance Programme). Please visit e2i’s website to find out more.
To enquire, email soc-ace@nus.edu.sg
To register, click Register
Course Code:
TGS-2020504368
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Course Fee Breakdown
Singapore Citizens
39 years old or youngerSingapore Citizen
40 years old or older
