Course Briefing

Specialist Diploma in

Applied Artificial Intelligence

Tin Aung Win (Course Manager)

Why Learn AI?

AI in National Agenda

InfoComm Media Transformation Map (ITM) by IMDA

Learning Outcomes of SDAAI

The course is carefully designed for participants with Science, Technology, Engineering or Mathematics (STEM) background. Upon graduating from the course, you will be able to:• Understand and explain artificial intelligence principles and

practices, and its applications in different business domains • Perform data gathering, extraction, transformation, visualization,

training and testing for building machine learning models• Develop, test and deploy AI solutions using machine learning and

deep learning algorithms, AI services APIs, software frameworks and tools, in different problem domains

• Analyse, tune and optimize the data set, machine learning and deep learning models for performance improvements

Specialist Diploma in Applied AI

• Second batch commencing on 20 April 2020• Applications closed on 16 Mar 2020• Class size of up to 30• Total Course Duration of 300 hours • Consists of two Post Diploma Certificates (150 hours each):

• PDC in AI Foundation and Machine Learning• PDC in Deep learning and AI Applications

• Total 10 modules (30 hours each)• 8 course-work and 2 project modules

• No final examinations for all modules but appropriate regular assessments

Entry Requirements

• Science, Technology, Engineering or Mathematics (STEM) Degree graduates or experience in any one of the domains; or

• Having attended a Specialist/Advanced Diploma in Data Analytics or related courses; or

• Polytechnic diploma graduates in IT, Engineering or related disciplines, with a minimum of 2 years' relevant working experience.

• Applicants who do not meet the entry requirements may be considered for admission to the course based on case-by-case evaluation:

• The Polytechnic reserves the right to shortlist and admit applicants.

Course Fees

https://www.nyp.edu.sg/schools/sit/lifelong-learning/specialist-diploma-in-applied-artificial-intelligence/entry-and-application.html

PDC in AI Foundation and Machine Learning:20 Apr – 26 Aug 2020 - Course Schedule

Week Introduction to Artificial Intelligence

Data Science Foundation Essentials of

Machine Learning 1 20-Apr-20 22-Apr-20 23-Apr-20

2 27-Apr-20 29-Apr-20 30-Apr-20

3 4-May-20 5-May-20 6-May-20*

4 11-May-20 13-May-20 14-May-20

5 18-May-20 20-May-20 21-May-20

6 26-May-20* 27-May-20 28-May-20

7 1-Jun-20 3-Jun-20 4-Jun-20

8 8-Jun-20 10-Jun-20 11-Jun-20

9-10 Holiday-Break 15-26 Jun 2020

11 29-Jun-20 1-Jul-20 2-Jul-20

12 6-Jul-20 8-Jul-20 9-Jul-20

Machine Learning Algorithms

Machine Learning Project

Machine Learning Algorithms

13 13-Jul-20 15-Jul-20 16-Jul-20

14 20-Jul-20 22-Jul-20 23-Jul-20

15 27-Jul-20 29-Jul-20 30-Jul-20

16 3-Aug-20 5-Aug-20 6-Aug-20

17 11-Aug-20* 12-Aug-20 13-Aug-20

18 17-Aug-20 19-Aug-20 20-Aug-20

19 24-Aug-20 26-Aug-20

https://www.nyp.edu.sg/content/dam/nyp/schools-course/sit/life-long-learners/specialist-diploma-in-applied-artificial-intelligence/sdaai_batch_2nov2019.pdf

PDC StructurePDC in AI Foundation & Machine Learning

No Module Name Module Hours

1 Introduction to Artificial Intelligence 30

2 Data Science Foundation 30

3 Essentials of Machine Learning 30

4 Machine Learning Algorithms 30

5 Machine Learning Project 30

Duration of PDC-1 150

PDC StructurePDC in Deep Learning and AI Applications

No Module Name Module Hours

1 Foundations of Deep Learning 30

2 Deep Learning Networks 30

3 AI Application with Deep Learning 30

4 Applications Development Using AI Services 30

5 Deep Learning Project 30

Duration of PDC-2 150

Language and Tools

• Language: Python• Environment: Jupyter• Container: Docker• Frameworks/Libraries: Scikit-Learn, Numpy, Pandas, Keras,

Scrapy, Matplotlib, Tensorflow, MLXtend,Spark, and others

AI Services: Google Cloud ML, Microsoft Cognitive Services

Hardware: GPU ( Cloud/Native)

Introduction to Artificial Intelligence

You will learn:

• How Artificial-Intelligence-based methods are applied to various problem domains

• Different AI approaches and techniques by comparisons of classical and modern AI systems

• Math Fundamentals

• The ethical and legal aspects of AI technologies using case studies

Data Science Foundation

You will learn:• How to identify and select the right data sources and data types for

collection • How to apply the data gathering, extraction and transformation

techniques to process data based on the data modelling and visualization requirements

• How to apply software frameworks and tools to process and manage the big data

• issues relating to data privacy and security in the context of AI

Essentials of Machine Learning

You will learn:• Different categories of machine learning problems and

algorithms using relevant application examples• Common machine learning issues and apply appropriate

techniques to overcome them• How to analyse input data for feature extraction in training

machine learning models• How to evaluate the performance of machine learning models

using appropriate metrics

Machine Learning Algorithms

You will learn:• Essential theories and key concepts of different machine

learning algorithms• How to apply different techniques to diagnose learning issues

to improve machine learning model• How to apply dimensionality reduction and tuning techniques

to speed up the training• How to apply appropriate machine learning algorithms for

different AI problems

Machine Learning Project

• What you will perform?• Implement a practical machine learning system that solves a real-

world problem in a selected domain by applying skill sets learnt from the course work modules.

• Group of maximum 4 students• Project group will be mentored by a supervisor

Foundations of Deep Learning

You will learn:– different types of deep learning networks in terms of its

underlying architecture and applications in related problem domains

– basic neural network architecture with reference to its building blocks and training and inference steps

– How to apply different optimization techniques to improve learning of deep learning network

– How to build and train a basic deep learning network using appropriate deep learning frameworks

Deep Learning Networks

You will learn– the underlying concepts of Convolutional Neural Networks

(CNN) and Recurrent Neural Network (RNN) and their applications

– How to apply Convolutional Neural Networks (CNN) for computer vision problems

– How to Apply Recurrent Neural Network (RNN) for sequence-based problem

– the blended CNN and RNN usage scenarios

AI Application with Deep Learning

You will learn:– the various AI applications that are used in solving real-world

problems– How to formulate suitable deep learning model for the given

problem and data set– How to apply appropriate techniques to address the specific

requirements in selected AI application domains.– How to develop deep learning applications using appropriate

software framework and tools

Applications Development Using AI Services You will learn:

– the AI services models and the use of APIs for AI application development

– How to evaluate different AI services with regard to its suitability for different application domains

– How to develop domain-specific applications using AI services

Deep Learning Project

What you will perform?

– You will apply the knowledge and skills to implement a practical deep learning system that solves a real-world problem in a selected domain

– By implementing a deep learning system, learners will have an opportunity to consolidate the various deep learning concepts

– Group of maximum 4 students– Each group will be mentored by a supervisor