Big Data Analytics, Machine Learning, and Artificial Intelligence

Big Data Analytics, Machine Learning, and Artificial Intelligence

Course Mapping

In the second common course of this semester, we will introduce Big Data Analytics, Machine Learning, and Artificial Intelligence which are growing faster and popular field of technology nowadays. For the Computer Science and Information Engineering department, they classify those subjects as advance courses, and focus on distributed file system, clustered system, and algorithm of machine learning. However, this course arrangement would not be suitable for students who are non- science and information engineering background. Thus, from business data analytics perspective, we will focus on Big Data Analytics、Machine Learning and Artificial Intelligence.

Big Data Analytics Course:

This course applies Business Analytics Platform from College of Management, NSYSU to provide comprehensive big data analytics cluster and machine learning system. Moreover, we also develop a series of demo programs and programming templates of network distributed file system, parallel clustered computing and machine learning. Students can easily use the URL, data frame interface and computing data by login account.

Machine Learning Course:

We prefer to see machine learning model as a black box, covering the complexity of algorithms, and models inside. Our Course will focus on supervised and unsupervised learning process operation and making basic machine learning process into template program. Students can practice different machine learning methods under the helping of (semi)automated program by editing templates.

Artificial Intelligence Course:

Our teaching will also focus on introducing different kinds of artificial neural networks and business applications. To consider the most students are non-IT background, we would use high level program interface (Keras package of R programming) and provide template program to them. The application of artificial intelligence in business data analytics and engineering are different. In engineering field, it attaches importance of real-time situational identification and automatic control. In business data analytics field, artificial intelligence model, deep learning model, and machine learning model are the same and be applied to predict.

In addition to these three technology fields, application of cloud resource is another teaching focal point. In this Course, we will build a text analytics platform and use free trial account of online analytics tool to teach student to do text analytics. Moreover, we will use deep learning in the cloud workshop to teach students how to use Google Cloud Platform (GCP) to rent and build a deep learning host and create a deep learning model in cloud.

Teaching Goals

As the primary teaching goal of this course, from the point of view of business applications, we hope that students feel that big data is not difficult, as long as using right tools and the right method. There is no difference between processing billions of data and processing thousands of data. Also, let them learn how to find out useful information from predictor variables. Therefore, the teaching point of this program is learning to use right tool flexibly to solve problems in different business situations rather than creating a strongest prediction model.

Course Outline

PART-I Big Data Analytics and Machine Learning

  • Basic Introduction of Machine Learning
    • Resource, Method and Model
    • Training Errors and Testing Errors
    • Parameter Tuning and Supervised/Unsupervised Learning
    • Ensemble Learning
  • Big Data Analytics and Basic Distributed Computing
    • Introduction of Distributed File System
    • Introduction of Hadoop, Spark, and H2O
    • Business Analytics Platform from College of Management, NSYSU
    • Case of Big Data Analytics
  • Unstructured Text Analysis
    •  Introduction of Unstructured Data
    • Text Analysis
    • Application of Text Analysis and High-Level Tool
    • Application of Basic Deep Learning
    • Deep Learning Model
    • Text/ Facial Recognition
    • Object Recognition

PART-II The applications of Big Data and Cloud Resource

  • Business Applications
    • Customer Value Management
    • Marketing Data Analytics
    • Product Selling Information
  • Cloud Resource Applications
    • Introduction of Business Analytics Platform from College of Management, NSYSU
    • Text Analytics Platform
    • Automated Online Analytic Tool
  • Deep Learning in the Cloud Workshop
    • Online Simulation: Neural Network Model
    • Build a Google Cloud Platform
    • Cases and Applications

Self-Made Material

This course does not use textbooks, we will build on past self-made teaching materials, according to the needs of the curriculum re-preparation of teaching materials. The teaching materials include:   

  • Course Website：Integrate self-made teaching materials and online resources to promote mutual learning (HTML)
  • Preview Video：To help students understand online preview content
  • Teaching Material：PowerPoint slide of every unit
  • Class Note：In-class R-Notebook of every unit
  • Homework Note：Homework R-Notebook of every unit
  • Online Simulate Program：Help students understand more complex concepts in an interactive simulation (R: Shiny)
  • Class Video： It is convenient for students to review or make up course. (YouTube)

Online Course

According to the teaching materials that we made, we will ask students to complete following online courses (self-learning units) on DataCamp for Classroom

We will choose appropriate teaching units, homework exercise or case study as preview materials or cite it in our teaching materials from those online course.

Reference

Here are reference of this course：

  1. EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data. John Wiley & Sons, 2015.
  2. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning: with Applications in R, 6 edition., 2013 (Available free online: http://www-bcf.usc.edu/~gareth/ISL/)
  3. Hwang and M. Chen, Big-Data Analytics for Cloud, IoT and Cognitive Computing, 1st ed. Wiley Publishing, 2018
  4. F. Chollet and J. J. Allaire, Deep Learning with R, 1 edition. Manning Publications, 2018