Course Identification

Introduction to Mathematical Methods for Modeling and Data Analysis
20192091

Lecturers and Teaching Assistants

Dr. Eli Galanti
Dr. Keren Milner

Course Schedule and Location

2019
First Semester
Monday, 09:15 - 11:00, FGS, Rm A

Tutorials
Wednesday, 12:15 - 13:00, FGS, Rm B
05/11/2018

Field of Study, Course Type and Credit Points

Chemical Sciences: Lecture; Elective; Core; 3.00 points
Chemical Sciences (Materials Science Track): Lecture; Elective; 3.00 points
Life Sciences (Molecular and Cellular Neuroscience Track): Lecture; Elective; 3.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Lecture; Elective; 3.00 points

Comments

N/A

Prerequisites

University level introductory courses in linear algebra and calculus

Restrictions

30

Language of Instruction

English

Attendance and participation

Required in at least 80% of the lectures

Grade Type

Numerical (out of 100)

Grade Breakdown (in %)

70%
30%

Evaluation Type

Final assignment

Scheduled date 1

13/02/2019
N/A
-
The Final assignment will be a home assignment.
It will be given on 13/02/2019 9am and should be submitted by 14/02/2019 5pm.

Estimated Weekly Independent Workload (in hours)

3

Syllabus

Mathematical models are present in all of the scientific disciplines , providing a quantitative framework for understanding and prediction of natural phenomena. The output from such models, as well as observations, often requires complex mathematical analysis. The course provides an introduction to mathematical modelling and data analysis through in depth discussion of a series of real examples, with an emphasis on 'hands on' exercises. 
Topics will include:
  • Ordinary differential equations and numerical solution
  • Linear equations and eigenvalue
  • Data analysis using EOF, PCA and SVD
  • Mining of Big Data
  • Advanced modelling and PDEs
  • Combining models and data - optimization

Learning Outcomes

Upon successful completion of this course students should be able to:

  1. Demonstrate an understanding of the  principles of mathematical modeling and data analysis
  2. Solve analytically and numerically a wide range of problem

Reading List

Strogarz, S.H. Nonlinear Dynamics and Chaos. Perseus books, 1994.

Website