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:
 Visualize data from a wide variety of sources.
 Analyze data using exploratory data analysis and clustering techniques.
 Understand and perform basic techniques in signal processing.
 Perform basic machine learning techniques.
Lecture Time: MWF 10:1011:00 (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 endtoend open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community
resources that lets researchers push the stateoftheart 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 5015052954.
Grading:
Assignment 
Weight 
A 
90100 
B 
8089 
C 
7079 
D 
6069 
F 
059 
Here are the semester's assignments and the associated points for each:
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 & ChiSquare Test 
None 
02/01/2019 
Friday 
Decision Tree 
None 
02/04/2019 
Monday 
Random Forest 
Lab1 (Statistics and Plotting) 
02/06/2019 
Wednesday 
Support Vector Machines & Logistic Regression 
None 
02/08/2019 
Friday 
kNearest Neighbors Algorithm 
None 
02/11/2019 
Monday 
kMeans 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 
SelfOrganizing Maps 
None 
02/21/2019 
Thursday 
No class 
Lab2 (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) 
Onepage overview of your project task. 
03/15/2019 
Friday 
Spring Break (No class) 
Lab4 (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 
NewtonRaphson Method & Secant Method 
None 
03/29/2019 
Friday 
Optimization (Gradient Descent) 
Onepage 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) 
Lab5 (Optimization) 
04/22/2019 
Monday 
Lab Sections 
Roughdraft 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 