cs 4 Computer Science Bootcamp

cs 4 Computer Science Bootcamp

Winter 2018 - Credits: 4
Department of Computer Science
University of California Santa Barbara

Course Information

  • Instructor: Professor Çetin Kaya Koç         → Koç is pronounced as "Coach"  

  • Class Schedule: Mon, Wed 3:30pm-4:45pm
  • Classroom: Theater/Dance West (TD-W) 1701
  • Instructor's Office Hours: Tuesdays 2:00-4:00pm
  • Instructor's Office: HFH 1119

  • Lab Schedule:
    Friday 9:00am-9:50am, TAs: TBA
    Friday 10:00am-10:50am, TAs: TBA
    Friday 11:00am-11:50am, TAs: TBA
    Friday 12:00pm-12:50pm, TAs: TBA
    Friday 1:00pm-1:50pm, TAs: TBA
    Friday 2:00pm-2:50pm, TAs: TBA
  • Lab: PHELPS 3525

  • Teaching Assistants:
    Leonidas Eleftheriou (leonidas00@ucsb.edu)
    Lucas Bang (bang@cs.ucsb.edu)
    Michael Nekrasov (mnekrasov@cs.ucsb.edu)
  • TA Office Hours: TBA
  • TA Office: Trailer 936 Room 104

  • Please join the Piazza page for class discussions  
  • Check the class website and the Piazza page once a day

  • Slides and course notes are in the folder docx
  • Homework and Lab documents are in the folder hwlab

  • The grades: cs4.htm   (The Code is your PERM number mod 98773  

Announcements and Weekly Course Material

  • We will have 7 Lab and 7 Homework Assignments
  • Homework Assignments and Lab Reports are due Wednesdays 5pm
    Either, upload an electronic copy to a Dropbox folder (to be provided)
    Or, deliver a paper copy to the HW Box in HFH 2108
  • Electronic copy of your homework or lab report can be in Text, PDF or MS Word. You could also scan/pdf your handwritten work, however, do not submit small and/or low-resolution phone-camera images.

  • Midterm Exam: Monday, February 26, during class
  • Final Exam: Friday, March 23, 12-3pm, in class

  • You need the College of Engineering accounts in order to use the computers in the Lab
    Open your browser and click on this link: https://accounts.engr.ucsb.edu/create
    Enter your UCSBNetId and Password, then follow the steps to request your account

  • Lab Assignment 01: lab01.pdf -- This lab does not require a report submission.
    Participation in the lab is necessary and sufficient.

  • Homework Assignment 01: hw01.pdf -- due 5pm Wednesday Jan 24

Catalog Specification

An introduction to computational thinking, computing, data management, and problem solving using computers, for non-majors ONLY. Topics include coding basics, representing code and data using a computer, and applications of computing that are important to society.

Weekly Course Plan

  • Lecture 01: Data Representation
    What is information? How do we represent information? What is data? Analog versus digital.
  • Lecture 02: Representation, Computation, and Storing
    History of number representation and calculation. Binary, decimal, and hexadecimal number systems. History of calculation and computers. Memory technologies.
  • Lecture 03: Representation of Text
    Representation of simple text and structured text. Text encoding standards: 7-bit and 8-bit ASCII, Unicode, and UTF-8.
  • Lecture 04: Representation of Images
    Color, intensity, and pixels. Tradeoffs in representation. Image formats: headers, compression algorithms, Huffman coding.
  • Lecture 05: Representation of Sound
    Analog sound and digital sound. Sampling and quantization. Digitized music and distortion. Sound compression and decompression.
  • Lecture 06: Overview of Programming and Operating Systems
    Principles of programming. Programming languages and platforms. Operating systems.
  • Lecture 07: Programming Practice
    A simple introduction to Python. Input, Output, and Debugging. Repetition and conditional statements.
  • Lecture 08: Iteration
    Iterative computations of sums and products. Computing sums of products and or products of sums. Computation of constants such pi or e.
  • Lecture 09: Recursion
    Types of recursion. Recursive algorithmic paradigms. Numerical examples and time/space estimates.
  • Lecture 10: Universal Computation
    The Arithmetic Model of Computer. Time/Space and Determinism/Nondeterminism, Difficulty of problems and hierarchies of problems. Limits of computation and decidability.
  • Lecture 11: Basic Arithmetic Algorithms
    Addition of integers. Various adders: carry-propagate, carry-save, and carry-lookahead. Multiplication using add-shift methods. Simple recursive algorithms and Karatsuba.
  • Lecture 12: Advanced Arithmetic Algorithms
    Cyclic and Linear Convolution. Fourier and Fast Fourier Transformation. Error-Detecting and Error-Correcting Codes.
  • Lecture 13: Searching and Sorting
    Ordering data. Data structures. Linear search and binary searc. Heap and binary tree. Sorting by searching. Bubblesort, Heapsort, Quicksort, and Mergesort.
  • Lecture 14: Analytical versus Numerical Computation
    Solving differential equations using analytical methods. Making sense of the solutions. Solving differential equations using numerical methods. Euler, Runge-Kutta, and pitfalls of numerical computation. Hilbert matrix and Wilkinson polynomial.
  • Lecture 15: Numerical and Symbolic Computation and Tools
    Products, sums and solving equations symbocally. Matrix computations. Matlab and Mathematica.

Weekly Lab Plan

  • Lab 01: Introduction
    The purpose of the lab is to obtain your COE account and to locate and experiment with some applications that we will need in the subsequent labs. The computer lab is located in Phelps 3525. We will help you to create your account and experiment with Linux, Python, and Mathematica.
  • Lab 02: Text Representation
    Students get CSIL accounts or use their own laptops. Illustrate how a simple text is a collection of bits. Similar illustrations of text in other languages (Latin, Arabic, Chinese, and Japanese). Illustrate structured text (such as MS Word documents) representation.
  • Lab 03: Computing Using Python
    Introduction to Python programming language. Basic data types. Iterative computation. Computing Pi.
  • Lab 04: Computing with Images
    Illustrate how a simple image is represented using cImage (Python). Color schemes and RGB. Simple image processing: Grayscaling, resizing, edge detection. Image compression and watermarking.
  • Lab 05: Recursive Computing and Fractals
    Install and use Python and turtle module to illustrate recursive algorithms and programs. Koch curves and Hilbert Curves.
  • Lab 06: Scientific Computing Tools
    Illustrate Matlab and Mathematica. Infinite precision computation. Solving equations. Plotting. Algebraic operations, symbolic computation, factoring, simplification.

Grading Rules

  • Lab Participation and Reports: 25 %
  • Homework Assignments: 25 %
  • Midterm Exam: 20 %
  • Final Exam: 30 %

Academic Integrity at UCSB  

Dr. Çetin Kaya Koç