Syllabus


The following is a tentative syllabus for the course and this page will be updated irregularly. On the other hand, the weekly syllabus contained in the Homework Assignments page will always be accurate.


Lectures Sections in Text Brief Description
Week 1
9/11 (M) 1.1 Introduction to Mathematical Modeling
9/13 (W) 1.2 Difference Equations and MatLab
9/14 (W) Individual Student Meetings
9/15 (F) 1.3 Solution Methods and Examples
Week 2
9/18 (M) 1.3 Population Models
9/20 (W) 1.4 Systems of Difference Equations
9/22 (F) 1.4 Systems of Voting Data
Week 3
9/25 (M) 3.1-3.4 Modeling Errors and Least Squares
9/27 (W) Lecture Notes Data Examples
9/29 (F) Lecture Notes Preference Systems
Week 4
10/2 (M) Lecture Notes Axiomatic Voting Systems
10/4 (W) Lecture Notes Voting Examples and Tournaments
10/6 (F) 10.1-10.3, 10.7 Modeling with Games
Week 5
10/9 (M) 10.3-10.6 Two Player Games
10/11 (W) 10.3-10.6 Solution Methods and Nash Equilibrium
10/13 (F) Lecture Notes Iterated Games and Auctions
Week 6
10/16 (M) 6.1 Markov Chains
10/18 (W) Lecture Notes Absorbing Markov Chains
10/20 (F) No Class
Week 7
10/23 (M) Lecture Notes Applications of Probability Models
10/25 (W) Lecture Notes Applications of Probability Models
10/26 (Th) Lecture Notes Benford's law and human pseudorandomness
10/27 (F) 8.1-8.2 Modeling with Networks
Week 8
10/30 (M) 8 Network Centralities
11/1 (W) 8 Null Models
11/3 (F) 8 Modularity and Assortativity
Week 9
11/6 (M) Lecture Notes Network Clustering
11/8 (W) 11.1-11.2 Differential Equations Models
11/10 (F) Chapter 12 Systems of Differential Equations
Week 10
11/13 (M) Chapter 12 Autonomous Systems and the Lorentz Attractor
11/17 (F) Final Exam


Daryl DeFord
Last updated October 30, 2017