Overview
This 3-day course focuses on establishing the math and programming foundations for machine learning (ML) as well as introducing supervised learning techniques, a class of ML techniques based on learning by examples. This course is recommended to be taken as a continuation from Machine Learning I or as a standalone for attendees with the recommended foundation.
At the end of the 3-day course, you will be able to:
- Understand the essential math required to analyse ML algorithms
- Understand the essential python programming knowledge to implement ML algorithms
- Apply supervised learning techniques to solve sample problems using python
- Implement a basic ML application using python as part of an individual project
To aid companies in transforming their capabilities through their human capital, and support Singapore’s drive towards becoming a Smart Nation, this course is mapped to the Singapore Economic Development Board’s Singapore Smart Industry Readiness Index:
- Dimension 10 - Shop Floor Intelligence
- Dimension 11 - Enterprise Intelligence
- Dimension 12 - Facility intelligence
What You’ll Learn
Revision on ML Background
- Motivation for ML
- Examples of ML Applications
- Individual Project Introduction
Primer on Math for ML
- Linear Algebra
- Statistics
- Calculus
- Project Conceptualisation
Primer on Python Programming for ML
- Basic Programming Constructs
- Data Structures and Algorithms
- Project Coding Environment Setup
Regression
- Concept of Predicting Continuous Values
- Linear Regression
- Gradient Descent
Classification I
- Concept of Predicting Discrete Labels
- Naive Bayes
- Support Vector Machines (SVMs)
- K-nearest Neighbours (k-NN)
- Project Implementation
Classification II
- Decision Trees
- Artificial Neural Networks (ANNs)
- Deep Learning
- Continuation of Project Implementation
Putting It All Together
- Finalising Project Implementation
- Project Sharing and Discussion
Who Should Attend
- Professionals who wish to explore possibilities of how technology can be utilised to optimise their business processes
- Professionals who wish to have a slightly deeper appreciation for machine learning or artificial intelligence
- Professionals embarking on projects which are either very data heavy or will require the use of machine learning
- Developers looking to incorporate machine learning into their existing projects
SITizens Learning Credits (SLC) - Eligible Course
This course is SITizens Learning Credits (SLC) eligible. Please refer to the
user guide how to register for courses utilising your SLC.
Find out more about
SITizens Learning Credits (SLC).
Prerequisites
Certificate and Assessment
A Certificate of Participation will be issued to participants who:
- Attend 75% of the course, and
- Undertake non-credit bearing assessment during the course
Schedule
| Day |
Date |
| Day 1 |
3 May 2023 |
| Day 2 |
10 May 2023 |
| Day 3 |
17 May 2023 |
Fees
| Category |
Full Fee |
After SF Funding |
| Singapore Citizen (Below 40) |
$2,568.00 |
$770.40 |
| Singapore Citizen (40 & above) |
$2,568.00 |
$290.40 |
| Singapore PR/ LTVP+ Holder |
$2,592.00 |
$777.60 |
| Non-Singaporeans |
$2,592.00 |
Not Eligible |
Note:
- All figures include GST. GST applies to individuals and Singapore-registered companies.
- You can opt for either SF Series Funding or Mid-Career Enhanced Subsidy. Both cannot be combined.
»
Learn more about funding types available
Terms & Conditions:
SkillsFuture Funding
In order to be eligible for the 70% training grant awarded by SkillsFuture, applicants (and/or their sponsoring organisations where applicable) must:
- Be a Singaporean Citizen or Singapore Permanent Resident or LTVP+ Holder
- Not receive any other funding from government sources in respect of the actual grant disbursed for the programme
SkillsFuture Mid-Career Enhanced Subsidy
To be eligible for the 90% enhanced subsidy awarded, applicants (and/or their sponsoring organisations where applicable) must:
- Be a Singaporean Citizen
- Be at least 40 years old
- Not receive any other funding from government sources in respect of the actual grant disbursed for the programme
SIT reserves the right to collect the balance of the programme fees (i.e. the potential grant amount) directly from the applicants (and/or their sponsoring organisations where applicable) should the above requirements not be fulfilled.
SIT reserves the right to make changes to published course information, including dates, times, venues, fees and instructors without prior notice.
