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Fall 2026

The Syllabus document with more details will be shared on Blackboard. Note that both the document and this website can be updated throughout the semester. Any disrepancies should be resolved by asking a question on Blackboard Discussion page.

Schedule

Day Time 10425 10426 10428 10429 10430 11225
Mon 08:30 E125 E125 E125 E125 E125 E125
Tue 08:30 B302
Tue 13:00 B102
Thu 08:30 B302
Thu 13:00 B201
Fri 10:00 B302
Fri 13:00 B102

Course Outline

Important

The content is subject to change. Please consistently check the course page on Blackboard and the ADA University Academic Calendar for modifications. The last day of the add/drop period, holidays, and similar dates are noted in the calendar.

Week Topic Learning Outcomes Assessment / Notes
1 Course Overview / Syllabus / Technology & Engineering Describe the course structure and ground rules as defined in the syllabus. Define and describe the challenges of technology and engineering.
2 Data, Information, Knowledge / Statistics / Systems / Information Systems Differentiate between data and information. Describe the role of statistics. Define a system, its components, and interactions. Describe information systems and components of computer-based IS.
3 Digital vs Analog / Data Units / Number Systems / Binary Arithmetic / Two’s Complement / Transistors / Boolean Logic Compare digital and analog systems. Define bit, byte, and data units. Convert numbers between decimal, binary, and hexadecimal. Explain binary addition, subtraction, and two’s complement. Describe Boolean logic, truth tables, transistors, and logic gates. Project 1 out [Team]: Constructing basic logic gates using transistors and other electronic components
4 Hardware: CPU, GPU, Memory, I/O, Storage Explain von Neumann architecture. Describe CPU components and operation. Explain GPU purpose. Differentiate memory types (ROM, RAM, cache), DRAM vs SRAM, and secondary storage technologies.
5 Algorithms I: Name Binding, Selection, Repetition Define algorithms. Explain basic algorithmic actions: name binding, selection, repetition. Project 2 out [Team]: Hour of Code
6 Algorithms II: Modularization, Recursion / Dijkstra’s Algorithm Explain modularization and recursion. Describe Dijkstra’s shortest path algorithm.
7 Software / Software Engineering / Programming Languages / Compilers & Interpreters / Operating Systems Define software and software engineering. Explain best practices in software development. Differentiate syntax vs semantics. Explain programming paradigms and language evolution. Compare compiler and interpreter. Describe OS roles and activities.
Midterm Exam: Oct 31 (Saturday)
8 Telecommunications / Computer Networks / OSI Model / Network Topologies Describe telecommunication models and media. Explain network classifications and characteristics. Describe computer networks and OSI layers. Compare network types and topologies. Project 3 out [Team]: Programming LEGO robots
9 Internet / IP Suite / Addressing / DNS / Routing / Intranet & Extranet / Distributed Systems Describe Internet structure. Explain IP suite, IP and MAC addresses. Describe DNS and routing. Define intranet, extranet, and distributed systems.
10 World Wide Web / Markup Languages / Cloud Computing / System Architectures Describe WWW structure and HTTP. Explain URLs. Describe markup languages. Explain cloud computing models and centralized vs decentralized systems.
11 Databases / Data Models / Indexing / Normalization / Big Data Explain database approach and advantages. Describe ER modeling and indexing. Explain relational model and normalization. Define Big Data and knowledge discovery.
12 DBMS / SQL / NoSQL / Transactions / ACID / CAP Explain DBMS types and purposes. Describe data manipulation operations. Explain SQL commands. Contrast SQL and NoSQL. Explain transactions, ACID properties, and CAP theorem. Project 4 out [Individual]: Building personal portfolio website
13 AI / ML / DL / Regression / Neural Networks / LLMs Define AI, ML, DL. Explain regression and classification. Describe supervised vs unsupervised learning. Explain linear regression and RMSE. Describe neural networks, LLMs, CSP backtracking, minimax, and reinforcement learning.
14 Systems Development / SDLC / Waterfall / Agile / Prototyping / Testing / UML Identify SDLC phases, participants, and outputs. Compare SDLC models. Explain DevOps and software testing. Describe UML diagrams (use case, class, sequence).
15 Engineering Ethics / Security / Privacy / Logic / Digital Hygiene Explain engineering ethics and social responsibility. Describe logical reasoning and fallacies. Outline ACM Code of Ethics. Explain digital hygiene and social issues of information systems.
Final Exam: Dec 26 (Saturday)