SIT Learn
SIT Learn

Overview

This 7-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 7-day course, you will be able to:

  1. Understand the essential math required to analyse ML algorithms
  2. Understand the essential python programming knowledge to implement ML algorithms
  3. Apply supervised learning techniques to solve sample problems using python
  4. Implement a basic ML application using python as part of an individual project

Speakers / Trainers

What You’ll Learn

Lesson 1: Revision on ML Background
  • Motivation for ML
  • Examples of ML Applications
  • Individual Project Introduction

Lesson 2: Primer on Math for ML
  • Linear Algebra
  • Statistics
  • Calculus
  • Project Conceptualisation

Lesson 3: Primer on Python Programming for ML
  • Basic Programming Constructs
  • Data Structures and Algorithms
  • Project Coding Environment Setup

Lesson 4: Regression
  • Concept of Predicting Continuous Values
  • Linear Regression
  • Gradient Descent

Lesson 5: Classification I
  • Concept of Predicting Discrete Labels
  • Naive Bayes
  • Support Vector Machines (SVMs)
  • K-nearest Neighbours (k-NN)
  • Project Implementation

Lesson 6: Classification II
  • Decision Trees
  • Artificial Neural Networks (ANNs)
  • Deep Learning
  • Continuation of Project Implementation

Lesson 7: 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

SIT Alumni: Before registering for courses, please activate your SITizens Learning Credits via the email sent by SIT Alumni Team, on behalf of SITLEARN Professional Development on 31 Jan 2019.

CLICK HERE TO APPLY (only for SIT Alumni)

Registration closes on 03/06/19
 

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

Week Date
Week 1 22 Jul2019  
Week 2 29 Jul 2019  
Week 3 5 Aug 2019  
Week 4 13 Aug 2019  
Week 5 19 Aug 2019  
Week 6 26 Aug 2019  
Week 7 2 Sep 2019  

Fees

Category Full Fee After SF Funding After SF Mid-Career
Enhanced Subsidy
Singapore Citizen (Below 40) / Singapore PR $2,568.00 $770.40 Not Eligible
Singapore Citizen (40 & above) $2,568.00 $770.40 $290.40
Non-Singaporeans $2,568.00 Not Eligible 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:
  1.  Be a Singaporean Citizen or Singapore Permanent Resident
  2.  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:
  1.  Be a Singaporean Citizen
  2.  Be at least 40 years old
  3.  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.

Key Info

Venue SIT@Dover, 10 Dover Drive S138683
Time 09:00 AM to 12:30 PM
Date 22 Jul 2019 (Mon)
02 Sep 2019 (Mon)

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