IE7275 Summer 2020 Northeastern University • Boston Past

Data Mining in Engineering

r python

This course covers the theory and applications of data mining in engineering. It reviews fundamentals and key concepts of data mining, discusses important data mining techniques, and presents algorithms for implementing these techniques.

Tue 1:20pm – 03:00pm ET

05/04/2020 – 06/25/2020

Room 409, 225 Terry Ave, or Zoom, Boston

4 Credits

Upcoming Deadlines

No upcoming deadlines at this time

Course Platform

Canvas

Course materials and grades

Piazza

Course discussions

Course Materials

Data Mining and Analysis: Fundamental Concepts and Algorithms (DMA)

Recommended

Author: Mohammed J. Zaki and Wagner Meira Jr. Url

Introduction to Data Mining (IDM)

Recommended

Author: Pang-Ning Tan, Michael Steinbach, and Vipin Kumar Url

Data Mining for Business Analytics: Concepts, Techniques, and Applications in R (DMBA)

Additional

Author: Galit Shmueli, et al. Url

An Introduction to Statistical Learning (ISL)

Additional

Authors: Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani Url

The Elements of Statistical Learning (ESL)

Additional

Authors: Trevor Hastie, Robert Tibshirani, and Jerome Friedman Url

Machine Learning (ML)

Recommended

Author: Tom M. Mitchell Url

Deep Learning (DL)

Recommended

Authors: Ian Goodfellow, Yoshua Bengio, and Aaron Courville Url

Linear Algebra

Supplementary

Author: G. Strang. Introduction to Linear Algebra. Wellesley-Cambridge Press, 2009. Ch 1-4. Url

R Markdown (RMD)

Supplementary

Author: Xie, Yihui, et al. Url

R for Data Science (R4DS)

Supplementary

Author: Hadley Wickham and Garrett Grolemund Url

Teaching Staff

Zhenyuan Lu, PhD

Instructor

Deep Learning Scientist and AI/ML Adjunct Faculty

Zoom

Office Hours: Tue 1:00pm to 2:00pm ET

Course Schedule

Date	Topic	Materials	Assignments
Week 1: Introduction
May 4	Introduction	Instructor's Brain DMBA Introduction
May 5	Basics of R	DMBA Overview of Data Mining Process DMA Chapter 1
May 6	Exploratory Data Analysis and Data Transformation	R4DS Exploratory Data Analysis Link DMBA Data Visualization (additional) R4DS Chapters 1-4 Additional
Week 2: Dimension Reduction and Evaluating Predictive Performance
May 11	Dimension Reduction	DMBA Dimension Reduction Book ISL Chapter 6.3.1 Chapter
May 13	Evaluating Predictive Performance	DMBA Evaluating Predictive Performance (additional) ISL Chapter 2.2; DMA Classification Assessment Additional	HW1 Due Due May 14 @23:59 ET
Week 3: Regression and Midterm Review
May 18	Multiple Linear Regression	DMBA Multiple Linear Regression (additional) ISL Chapter 3 Linear Regression Additional
May 20	Midterm Review	Dimension Reduction Evaluating Predictive Performance Multiple Linear Regression
May 21	k-Nearest Neighbors	DMBA k-Nearest Neighbors Book (additional) DM Chapter 5.2; ESL Chapter 11.3 Additional	HW2 Due Due May 21 @23:59 ET
Week 4: Classification Techniques
May 26	Naïve Bayes Classifier	DMBA Naive Bayes Classifier Book (additional) ESL Chapter 6.6.3; DMA Chapter 18 Additional
Week 5: Advanced Techniques
Jun 1	Logistic Regression, Generative vs. Discriminative Models	DMBA Logistic Regression Book (additional) ISL Chapter 4.3; ESL Chapter 4.4 Additional
Week 6: Deep Learning and Neural Networks
Jun 9	Linear Discriminant Analysis	DMBA Linear Discriminant Analysis Book (additional) DMA Chapter 20 Additional
Jun 10	Neural Networks and Deep Learning	DMBA Neural Nets Book (additional) DL Chapter 6 Additional	HW4 Due Due Jun 13 @23:59 ET
Week 7: Final Review and Submission
Jun 17	Association Rule and Clustering Analysis Guest Topic: Industry in a Nutshell Guest: Jinhui Zhao @TikTok Guest Topic: Industry in a Nutshell Guest: Qibin Tan @iRobot	DMBA Association Rule Book DMBA Clustering Analysis Book	Project Slides and Files Due Jun 17 @23:59 ET
Final Week
Jun 25	Final Exam and Project Submission		Final Exam Due Jun 25 @23:59 ET

Polices

Plagiarism, cheating, and any form of unauthorized collaboration will not be tolerated and will be handled in accordance with University policies described in the Student Handbook. For additional information on Northeastern University’s Academic Integrity Policy.

Course Goals

Data Mining Work Flowl.

Accommodations for Students with Disabilities

If you have a disability, I encourage you to contact the Disability Resource Center to register and request accommodations. Also, please discuss your needs with me as early in the semester as possible.

Taking Care of Yourself

Eating healthy food, having regular exercise, avoiding alcohol and drugs, getting adequate sleep, and taking time to relax will help you achieve your goals and manage stress.

If you have difficulty keeping up with any materials or homework for personal reasons, please let me know early. If you or your friends/classmates appear to be struggling or having trouble coping with stress, we strongly encourage you to seek support at the We Care program at NEU. At Northeastern, a student is never alone when struggling with a demanding situation.

Homework

There are 6 individual homework assignments. Due day will be posted with the homework. Late submission would not be accepted. Please let me know 72 hours in advance before the due day if you need extensions with a reasonable justification. Requests for regrades in writing will only be accepted no less than 10 days after receiving grade. Please send the instructor your NUID, and name with title “Request for regrade: HW+number” via email. The new grade may be lower than the original one.

Please feel free to refer to any materials from my slides. You may discuss homework with your classmates, but all the assignments are supposed to completed by your own. Sharing of completed solutions will not be tolerated. Plagiarism will be considered, if solutions and project documentations with a very high degree of similarity with other student’s or materials online. Such academic dishonesty will be handled in accordance with university policies.