Data Analytics DA

Spring Semester
Abstract

Data analytics is now becoming the fashion in all domains. Related buzzwords, such as data mining, big data, artificial intelligence, machine learning, deep learning, are floating around in all kinds of media. In this course, we kick-off to understand the fundamental definitions behind all buzzwords as well as to learn the common techniques, such as multivariate statistical inference, and supervised/unsupervised learning algorithms. R language or Python will be used through this course in order to comprehend, compare, and link the different techniques to the practical world.

Objective

Students from this course shall learn to:

  1. understand the data characteristics and the fitness of different algorithms;
  2. pretreat and clean the data;
  3. extract and select significant features;
  4. explain the analytical results;
  5. use R/Python for quick data analytics.

Time Series Analytics TSA

Autumn Semester
Abstract

Time series and signals exist everywhere, and, in particular, the data collection and analysis are much easier than before with the advancement of modern information technology. This course starts by modeling the common time series, such as the demands, economic indicators. Digital signals, such as the machine sensor readings, ECG, and soundwaves are then analyzed with signal processing techniques. The goal is to develop a general sense of treating temporal signals.

Objective

Students from this course shall learn to:

  1. comprehend the characteristics of different time series and signals;
  2. understand the time series identification, estimation, and diagnostic;
  3. understand the analytical techniques for digital signal processing;
  4. apply proper treatments for analyzing time-series data.

Introduction to Semiconductor Intelligent Manufacturing Systems IMS

Spring Semester
Abstract

With the boosting development of information technology and data analytics, manufacturing science has also advanced rapidly. The industrial revolution that was driven by the collaboration of factory automation and ERP systems in the past has now evolved to the intelligent manufacturing systems and applications, triggered by high-performance cloud computing and machine learning algorithms. This course will combine the precious experience of industry lecturers with the academic professions of our faculty to integrate the theories and real practices of semiconductor manufacturing science in a comprehensive way. Students will be guided to understand the potential problems encountered in the production process and the optimal solutions currently available. Through case studying projects on the actual process, product, and equipment, students’ ability and experience to apply industrial engineering expertise in solving actual manufacturing domain problems will be developed in depth.

Objective

Students from this course shall learn to:

  1. comprehend the key functions of the individual modules within the advanced manufacturing systems;
  2. understand the importance of contemporary information and data analytics to intelligent manufacturing systems;
  3. realize the interrelationships among product quality, machine condition, and process efficiency;
  4. understand the intelligent systems in depth via practical project researches.