Data Analytics and Machine Learning

Half-Day Course - 12.30pm - 5.30pm

Course Description

This course covers the fundamentals of data analytics and machine learning. It will start with the fundamentals of analytics such as Bayesian statistics, classification,supervised learning, and unsupervised learning. The course will also introduce the participants to basic machine learning algorithms such as decision trees, support vector machines, and neural networks. The course will be hands-on with an emphasis on practical applications where real-life examples will be demonstrated using Python code.

Learning Objectives

This course will introduce the participants to the fundamentals of data analytics and machine learning. The topics covered will include: decision trees, Bayesian classification, regression, support vector machines, neural networks, and unsupervised learning techniques such as k-nearest neighbor. The participants will also be exposed to methodologies to evaluate the effectiveness of these techniques. The course will introduce the participants to the use of Python for data analytics and machine learning.

Learning Outcomes

1) Be familiar with the basics of data analytics and machine learning

2) Be able to formulate and use appropriate models of data analysis to obtain insights into problems

3) Be able to select appropriate models of analysis/learning, assess the quality of input and output, and investigate potential issues.  

4) Be able to think critically in making decisions based on data and analytics.

Who Should Attend

The target audience for this course are professionals who wish to gain familiarity and understanding of the important ideas in data analytics and machine learning. The course is also targeted at professional who want an exposure to the tools to get started in data analytics and machine learning.

Topics

1) Decision Trees

2) Bayesian classification

3) Regression

4) Support vector machines

5) Neural networks

6) Unsupervised learning

 

 

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Instructor
Dr. Biplab Sikdar
Date
24 January 2019
Venue
National University of Singapore
Status
Close for Registration
Course Fee
Standard Fee: SGD $428

* Fees indicated are inclusive of GST.

* Fees include course materials and afternoon tea break.

* The course date is subject to change. Please check TDSI website www.nus.edu.sg/tdsi for programme updates. 

* Participants who fully complete the half-day course may qualify for PDUs by the PEB.