Course Overview
This course focuses on establishing the math and programming foundations for machine learning (ML).
It also introduces 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 participants with the recommended foundation.
By the end of the course, you'll have a better understanding of the mathematics that underlies ML and the essential Python programming knowledge needed to implement ML algorithms. You will also be able to apply supervised learning techniques to solve sample problems using Python and 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
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
Prerequisites
- Attended Machine Learning I: An Introduction for Absolute Beginners OR
- Possess basic knowledge of the Python programming language; and
- Possess basic knowledge of ML fundamentals
What You Will 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
Teaching Team
Soh Cheng Lock, Donny
Associate Professor / Prog Leader, Infocomm Technology, Singapore Institute of Technology
Tan Chek Tien
Associate Professor, Infocomm Technology, Singapore Institute of Technology
Schedule
Time | Dates |
---|---|
9:00 am - 6:00 pm | 17, 24 Nov & 1 Dec 2023 |
Certificate and Assessment
A Certificate of Participation will be issued to participants who
- Attend at least 75% of the course
- Undertake and pass non-credit bearing assessment during the course
Fee Structure
The full fee for this course is S$2,616.00.
Category | After SF Funding |
---|---|
Singapore Citizen (Below 40) | S$784.80 |
Singapore Citizen (40 & Above) | S$304.80 |
Singapore PR / LTVP+ Holder | S$784.80 |
Non-Singapore Citizen | S$2,616.00 (No Funding) |
Note: All fees above include GST. GST applies to individuals and Singapore-registered companies.
Course Runs
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