ECE 657A - Winter 2017

ECE 657A - Data and Knowledge Modeling and Analysis

Instructor: Professor Mark Crowley
Office: E5 4114
Email: mcrowley@uwaterloo.ca
Office hours: To be determined or email me to arrange an appointment

Lectures

Wednesdays 11:30am - 2:20pm in room E5 5106 from January 4 to March 29, 2017

Course teaching assistants

To be announced

Course Description

Engineers encounter data in many of their tasks. Whether the sources of this data may be experiments, databases, computer files, or the Internet, there is a dire need for effective methods to model and analyze the data and extract useful knowledge and information from it. This course aims to provide engineering graduate students with essential knowledge of data representation, grouping, mining and knowledge discovery.

Recommended background

Data Structures and Algorithms, Probability, or consent of instructor.

Evaluation

  • 20% Assignments (two assignments, in groups)
  • 50% Exams
  • 30% Group project: (5% proposal, 8% presentation, 2% proposal/presentation peer reviews, 15% report)

Major Topics

  1. Data types, sources, nature, scales and distributions
  2. Data representations, transformation, feature extraction and selection, dimensionality reduction and normalization
  3. Classification: Distance based, Decision Tree based, Statistical based, Deep Learning.
  4. Clustering: Partition-based, Hierarchical, Model and Density based.
  5. Retrieval and Mining: Similarity measures and matching techniques.
  6. Knowledge discovery in data: Association rules mining, web mining, text mining.
  7. Performance measures and tools: Statistical Analysis, Validity and Assessment Measures.

Textbooks

There is no required textbook. But most of the course is based on the following books and will be useful to take a look at them.

  1. Margaret Dunham, Data Mining Introductory and Advanced Topics, ISBN: 0130888923, Prentice Hall, 2003.
  2. R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification (Second edition), John Wiley and Sons, 2001.
  3. K. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
  4. Jiawei Han, Micheline Kamber & Jian Pei, Data Mining: Concepts and Techniques, Third edition, Morgan Kaufmann Publishers, May 2011.

Papers and electronic references will be made available on the course website which is on LEARN.

Recipe for success

Attend lectures. Do complementary work at home. Ask questions. Have fun.

Policy and Rules

  • Academic integrity: In order to maintain a culture of academic integrity, members of the University of Waterloo community are expected to promote honesty, trust, fairness, respect and responsibility.
  • Grievance: A student who believes that a decision affecting some aspect of his/her university life has been unfair or unreasonable may have grounds for initiating a grievance. Read Policy 70, Student Petitions and Grievances, Section 4. When in doubt please be certain to contact the department’s administrative assistant who will provide further assistance.
  • Discipline: A student is expected to know what constitutes academic integrity to avoid committing an academic offence, and to take responsibility for his/her actions. A student who is unsure whether an action constitutes an offence, or who needs help in learning how to avoid offences (e.g., plagiarism, cheating) or about “rules” for group work/collaboration should seek guidance from the course instructor, academic advisor, or the undergraduate Associate Dean. For information on categories of offences and types of penalties, students should refer to Policy 71, Student Discipline. For typical penalties check Guidelines for the Assessment of Penalties.
  • Appeals: A decision made or penalty imposed under Policy 70 (Student Petitions and Grievances) (other than a petition) or Policy 71 (Student Discipline) may be appealed if there is a ground. A student who believes he/she has a ground for an appeal should refer to Policy 72 (Student Appeals).
  • Note for students with disabilities: The AccessAbility Services, located in Needles Hall, Room 1132, collaborates with all academic departments to arrange appropriate accommodations for students with disabilities without compromising the academic integrity of the curriculum. If you require academic accommodations to lessen the impact of your disability, please register with the AccessAbility Services at the beginning of each academic term.