CSE 201: Fundamentals of Information Technology

Spring 2013

Lecture:

Tuesday / Thursday 3:00-4:20 pm, in room 1145 Engineering Building

Enrollment:

Students in the IT Specialization should already be on a list to be enabled to enroll. Contact your adviser in Business, Communication Arts, or Engineering if interested in the IT specialization.

Course Description:

Digital representation of objects such as numbers, signals, and 3-dimensional shapes. Algorithms that operate on digital objects. Computer communications and the Internet. Computer security and web services.

Prerequisites: (CSE 101 or CSE 131) and (MTH 103 or MTH 116 or MTH 124 or MTH 132 or LBS 117)

Course Information: (Password Protected)

Course Outline

• Reviews:
  • Math
  • Computer organization
• Algorithms
  • Definition and examples
  • Introduction to Python
    • Square-root procedure, sorting, searching, cryptology
  • Complexity of an algorithm: Easy and seemingly hard problems
• Object Representation
  • Issues: Cost, Operation, Recovery, Transmission
  • Number Representation: Base Conversions
• Color Representation
  • Color Theory
    • Additive, subtractive colors,
    • Picture Resolution
    • Monitors and Printers
  • Human Vision capabilities and limitations
  • Videos, Movies, data storage
  • CAD/CAM, VRML
• Data Compression Techniques
  • Probability Theory  (random variables, expected value,…)
  • Entropy
  • Huffman Coding
  • Universal Encoding (Lempel-Ziv Scheme)
  • Image compression
    • Run Length Encoding
    • PCX, JPEG, TIFF, GIF
    • Lossy Compressions
• Data Communication
  • Signal: Analog, Digital, periodic signals, sinusoidal signals
  • Fourier Series
  • Bandwidth, Data rate, Channel capacity
  • Nyquist and Shannon Theorems
  • Signal encoding of data
  • Telephone systems
  • Transmission Media
  • Communication Satellites
  • Mobile Phone System
  • Internet
  • IP, TCP, DNS, HTML
  • Network Services qInformation Privacy
• Computer Security
  • Errors and their treatments
    • Error Correcting Codes
    • Hamming Error correcting code, Cyclic Redundancy Codes
• Data Mining
  • Finding patterns in data
    • Forecasting what may happen in the future
    • Classifying object (e.g., customers) into groups by recognizing patterns
    • Clustering objects into groups based on their attributes

Course Work:

• Reading assignments: Textbook, Papers, websites
• Weekly Homework assignments
• Writing short programs in Python

Instructor Information:

Instructor: Abdol-Hossein Esfahanian

Office: 3115 Engineering Building

Office hours: Wednesday 1:00-2:00 pm or by appointment

Any time my door is open, you are welcome.

phone: 432-9476; e-mail: esfahanian@cse.msu.edu

Teaching Assistant: Ronald Nussbaum

            Office: 3203 Engineering Building (Bone Lab)

            Office Hours: Tuesday / Thursday 10:30 - 11:45 am or by appointment

            e-mail: ronald@msu.edu

Course Information: (Password Protected)