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Overview

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Enquiry Please email to iss-blendedlearning@nus.edu.sg
A subset of artificial intelligence (AI), machine learning uses data and algorithms to imitate the way that humans learn. While simple rule-based automation is typically used for standardised and predictable processes, machine learning can handle more complex processes. They are also able to learn and improve from experience without being explicitly programmed, leading to greater improvements in accuracy and efficiency. 

In the competitive business landscape of today, machine learning is an increasingly powerful tool in helping organisations enhance their workflows through automation. By taking straightforward automated tasks and layering them in an element of prediction, it can help to solve problems at a speed and scale that cannot be duplicated by human efforts alone. Established cases of machine learning in the industries include fraud detection, medical diagnosis and e-commerce recommendation models. 

This introductory course in machine learning covers fundamental concepts, algorithms and techniques. Learners will learn how to uncover patterns to derive actionable insights. They can also expect engaging hands-on workshops to help them reinforce understanding of the subject matter. As a concluding team project, learners will work as a group to create and train a Neural Network model to classify images.   

Key Takeaways

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

● Gain insights on fundamentals machine learning concepts, algorithms and techniques to discover and analyse patterns for actionable outcomes. 

● Learn to use common software tools and libraries found in Machine Learning projects. 

● Build an Image Classifier to classify images; learners have the flexibility to use their own dataset to train their models. 

Structure

● Course duration: 4 weeks (online / face-to-face)

● Learners' time commitment: 6 to 7 hours per week. 

● This course has 7 synchronous live sessions - group activities, discussions, feedback and interactions. 

● Throughout the course, learners are expected to actively contribute to the group activities and are encouraged to apply the knowledge learnt.


Who Should Attend

This course is appropriate for professionals with some level of programming proficiency or knowledge of programming concepts albeit they are not required to know the Python programming language.

Instructors

TAN Cher Wah
Mr. TAN Cher Wah

Senior Lecturer & Consultant, Software Systems Practice

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Fees And Funding

Type of Learners  Course Fee Total course fee payable, including GST
Learners residing in Singapore (SGD) SGD 2500 SGD 2725
Learners residing outside Singapore (USD) USD 1850 USD 1850

Things To Note

● Learners are expected to set aside time for group activities before and beyond the course.

● The NUS-ISS Certificate of Completion will be issued to learners who have met the course requirements successfully. This will include attending all synchronous Zoom sessions and completing the learning activities, workshops and assessments (where applicable). 

● Read the terms and conditions of NUS-ISS Course Registration here.

● Read about NUS-ISS and Learner’s Commitment and Responsibilities here

● All classes are subject to confirmation and NUS-ISS will send an acceptance email to learners one week prior to the commencement date.

● All responses to feedback and surveys conducted by NUS-ISS and its partners must be submitted by learners. 

● All trainings and assessments will be delivered by NUS-ISS, as described in the course webpage.  

● For general enquiries and feedback, please feel free to reach out to us via email at iss-blendedlearning@nus.edu.sg.

This course will equip learners with the skills and best practices in the project management of an RPA/IPA project so that one can initiate, manage, deploy and scale RPA/IPA projects confidently and effectively.

This course equips learners with the knowledge to analyse data more effectively by deriving useful hidden patterns in it. Learners will also learn how to select and apply the most suitable techniques to solve problems and develop pattern recognition systems.

Artificial Intelligence

Machine Reasoning (Blended Learning)

This course allows learners to learn comprehensive knowledge of artificial intelligence (AI) fundamentals, automated computer/machine reasoning methods, knowledge discovery & modelling, decision support technologies, and intuitive graphics-based programming skills to design and create intelligent machine reasoning systems to solve real-world problems.