MATH3290 - Mathematical Modeling - 2017/18

Announcement

• No tutorial on 13 Sep (Wed).
• (Oct 18) Assignment 1 (Q5) is graded and returned to the assignment box.
• (Oct 19) The files for assignment 2 have been uploaded.
• (Nov 25) Assignment 2 has been graded and returned to the assignment box.
• (Nov 25) Assignment 3 Solution has been uploaded.
• (Nov 25) Assignment 3 (Q1) has been graded and returned to the assignment box.
• (Dec 8) Assignment 4 (Q3) has been graded and returned back to the assignment box.

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General Information

Lecturer

• Eric Chung
  • Office: LSB 205
  • Email:

Teaching Assistant

• Simon Pun
  • Office: LSB 222C
  • Tel: 39438570
  • Email:
  • Office Hours: M5-6, T1-3, H1-3
• Xia Wang
  • Office: LSB 222C
  • Tel: 39438570
  • Email:
  • Office Hours: M2-4, W2-4, H3-4

Time and Venue

• Lecture: Tuesdays 2:30-4:15pm at Lady Shaw Bldg LT6; Wednesdays 9:30-10:15am at Y.C. Liang Hall 104
• Tutorial: Wednesdays 8:30-9:15am at Y.C. Liang Hall 104

Course Description

This course is an introduction to mathematical modeling. We will cover some basic mathematical tools for the quantitative description of practical problems arising from physics, biology, economics and engineering. The use of these mathematical models allows us to quantitatively study and provide solutions to these problems. The focus of this course is to give an overview of the mathematical techniques that are commonly used in practice, and illustrate the modeling procedure through some elementary examples.

The following is an outline of this course.

1. Modeling change by difference equations

2. Model fitting and empirical modelling

3. Mathematical tools for big data analysis

4. Simulation modeling

5. Discrete optimization modeling

6. Modeling by graph theory

7. Continuous optimization modeling

8. Modeling with differential equations

Textbooks

• A First Course in Mathematical Modeling, by Giordano, Fox, Horton and Weir. 5th Edition.

Lecture Notes

• Chapter 2
• Chapter 1
• Chapter 3
• Chapter 4
• Chapter on Data Analysis
• Chapter 5
• Chapter 7
• Chapter 8
• Chapter 13
• Chapter 11
• Chapter 12

Class Notes

• Simple code for k means algorithm
• Sample questions
• Practice problems

Tutorial Notes

• MATLAB tutorial (last updated on 12 Sep)
• Tutorial 1 (last updated on 16 Oct)
• Tutorial 2 (last updated on 28 Sep)
• Tutorial 3 (last updated on 4 Oct)
• Tutorial 4 (last updated on 11 Oct)
• Tutorial 5 (last updated on 18 Oct)
• Tutorial 6 (last updated on 25 Oct)
• Tutorial 7 (last updated on 1 Nov)
• Tutorial 8 (last updated on 14 Nov)
• Tutorial 9 (last updated on 15 Nov)
• Tutorial 10 (last updated on 24 Nov)
• Tutorial 11 (last updated on 29 Nov)

Assignments

• Assignment 1
• Assignment 2
• Assignment 3
• Assignment 4

Solutions

• Solution 1
• Solution of Midterm Test
• Solution 2
• Solution 3
• Solution 4

Assessment Scheme

Assignments		10%
Midterm (Oct 17)		35%
Final Exam		55%

Honesty in Academic Work

The Chinese University of Hong Kong places very high importance on honesty in academic work submitted by students, and adopts a policy of zero tolerance on cheating and plagiarism. Any related offence will lead to disciplinary action including termination of studies at the University. Although cases of cheating or plagiarism are rare at the University, everyone should make himself / herself familiar with the content of the following website:

http://www.cuhk.edu.hk/policy/academichonesty/

and thereby help avoid any practice that would not be acceptable.

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Assessment Policy

Last updated: December 07, 2017 11:33:04