CSCI 285 - Scientific Computing

Spring 2019

Course Overview:

Students study problems arising from the physical, biological, and/or social sciences and the algorithms and theory used to solve them computationally. Included among the problems are numerical methods for maximizing a function and solving a differential equation. Prerequisite: MATH 130 and CSCI 150.

At the end of the course, you will be expected to be able to:

Lecture Time: MWF 10:10-11:00 (A3)

Room: MC Reynolds 110

Instructor: Dr. Bayazit Karaman, MC Reynolds 313

Office Hours: By appointment. To make an appointment with me, please send an email. Also, please feel free to stop by whenever my door is open.

Software:

R Programming Python TensorFlow
R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. Python is a general purpose programming language. Hence, you can use the programming language for developing both desktop and web applications. Also, you can use Python for developing complex scientific and numeric applications. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.

Revisions: After lab assignments are returned, you are welcome to revise and resubmit your work. Each submitted revision will be graded anew, the original and revised grades will be averaged to produce a new grade for that assignment. Revisions may be submitted anytime until the start of the final exam period.

Extensions: No late work will be accepted. Any work not submitted on time is a zero. However, you may submit a solution after the deadline to qualify under the revision policy. In effect, this means that late work can earn up to half credit.

Absences: You may miss four class days with no penalty. These can be for sports travel, school sanctioned activities, sick, etc. Every subsequent absence will result in a 4% penalty on your final grade.

Academic Integrity: All Hendrix students must abide by the College’s Academic Integrity Policy as well as the College’s Computer Policy, both of which are outlined in the Student Handbook.

For specific ways the Academic Integrity policy applies in this course, please refer to the Computer Science Academic Integrity Policy.

The short version is that academic integrity violations such as copying code from another student or the Internet are easy to detect, will be taken very seriously, and carry a default recommended sanction of a zero on the assignment in addition to a decrease of one letter grade on your final grade.

If you have any questions about how the Academic Integrity policy applies in a particular situation, please contact me.

Accommodations: It is the policy of Hendrix College to accommodate students with disabilities, pursuant to federal and state law. Any student who needs accommodation in relation to a recognized disability should inform the instructor at the beginning of the course. In order to receive accommodations, students with disabilities are directed to contact Julie Brown in Academic Support Services at 501-505-2954.

Grading:

Assignment Weight
A 90-100
B 80-89
C 70-79
D 60-69
F 0-59

Here are the semester's assignments and the associated points for each:

Weights
Midterm Exam 15%
Labs 35%
Final Project 50%

Schedule: The anticipated schedule for the semester is below. The instructor reserves the right to alter the schedule as necessary during the semester. Unless noted otherwise, each project is due at the start of class.

Date Day Topic/Activity Assignment Due
01/16/2019 Wednesday Go over syllabus None
01/18/2019 Friday Introduction to R programming None
01/21/2019 Monday Martin Luther King Birthday (No class) None
01/23/2019 Wednesday Introduction to R programming None
01/25/2019 Friday Using graphs in R & Linear Regression None
01/28/2019 Monday Interpretation of Linear Regression None
01/30/2019 Wednesday Multiple Regression & Chi-Square Test None
02/01/2019 Friday Decision Tree None
02/04/2019 Monday Random Forest Lab-1 (Statistics and Plotting)
02/06/2019 Wednesday Support Vector Machines & Logistic Regression None
02/08/2019 Friday k-Nearest Neighbors Algorithm None
02/11/2019 Monday k-Means Clustering None
02/13/2019 Wednesday Hierarchical Clustering None
02/15/2019 Friday Classification Review None
02/18/2019 Monday Principal Component Analysis (PCA) None
02/20/2019 Wednesday Self-Organizing Maps None
02/21/2019 Thursday No class Lab-2 (Classification)
02/22/2019 Friday Review for Midterm None
02/25/2019 Monday Midterm Exam None
02/27/2019 Wednesday Differential Equation Modelling None
03/01/2019 Friday Differential Equation Modelling None
03/04/2019 Monday Differential Equation Modelling Last day for selecting your partners and topic.
03/06/2019 Wednesday Image Analysis (TensorFlow) None
03/08/2019 Friday Image Analysis (TensorFlow) None
03/11/2019 Monday Project Idea Presentations (10 minutes for each group) None
03/13/2019 Wednesday Project Idea Presentations (10 minutes for each group) One-page overview of your project task.
03/15/2019 Friday Spring Break (No class) Lab-4 (Differential Equation Modelling)
03/18/2019 Monday Spring Break (No class) None
03/20/2019 Wednesday Spring Break (No class) None
03/22/2019 Friday Spring Break (No class) None
03/25/2019 Monday Optimization None
03/27/2019 Wednesday Newton-Raphson Method & Secant Method None
03/29/2019 Friday Optimization (Gradient Descent) One-page description of your resources.
04/01/2019 Monday Optimization (Simulated Annealing) None
04/03/2019 Wednesday Optimization (Travelling Salesman Problem) None
04/05/2019 Friday Electroencephalogram (EEG) None
04/08/2019 Monday Electroencephalogram (EEG) None
04/10/2019 Wednesday Signal Processing (Introduction) None
04/12/2019 Friday Signal Processing (Discrete Fourier Transform) None
04/15/2019 Monday Signal Processing (Independent Component Analysis) None
04/17/2019 Wednesday Random Numbers (Uniform Distribution) None
04/19/2019 Friday Random Numbers (Normal Distribution) Lab-5 (Optimization)
04/22/2019 Monday Lab Sections Rough-draft of your project paper
04/24/2019 Wednesday Final Project Presentations (Groups C and G) None
04/26/2019 Friday Final Project Presentations (Groups E, F and H) None
04/29/2019 Monday Final Project Presentations (Groups A, B and D) None
05/03/2019 Friday None Final Report