Course Information

• Batch: 72th
• Course Code: CSE-411
• Credits: 03
• Course Teacher: Md Habibul Basar Faruq
• Email: mh.faruq06@gmail.com
• Google Classroom: Join

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Course Description

This course introduces the principles and practices of parallel and distributed computing. Topics include models of parallelism, architectures, communication and synchronization, performance metrics, parallel algorithms, and distributed system fundamentals. Students will gain hands-on experience with frameworks such as MPI, OpenMP, Hadoop, and Spark, applying them to real-world problem solving.

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Weekly Topics and Resources

• Week 1: Introduction to Parallel and Distributed Systems
  Motivation, real-world applications, system architectures. | Lecture slide 01

Course Information

• Semester: 8th
• Course Code: CSE-4208
• Credits: 01
• Course Teacher: Md Habibul Basar Faruq
• Email: mh.faruq06@gmail.com

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Lab Objectives

• Implement and visualize basic computer graphics algorithms.
• Apply 2D and 3D transformations and projection techniques.
• Explore clipping, shading, texture mapping, and animation.
• Gain hands-on experience with OpenGL/GLUT for interactive graphics.
• Develop simple real-time graphics applications.

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Expected Outcomes

By the end of this lab, students will be able to:

• Write and execute programs to draw primitives and apply transformations.
• Implement algorithms for clipping, filling, and hidden-surface removal.
• Create interactive applications using OpenGL.
• Design mini projects with real-time rendering and animation.

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Weekly Activities

• Week 1: Introduction to Graphics Programming

Course Information

• Semester: 8th
• Course Code: 07-0613-CG802
• Credits: 03
• Course Teacher: Md Habibul Basar Faruq
• Email: mh.faruq06@gmail.com

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Course Description

This course introduces the principles and techniques of computer graphics. Topics include the graphics pipeline, 2D and 3D transformations, viewing and projections, rasterization, shading, texture mapping, ray tracing, geometric modeling, animation, and advanced rendering. Students will learn the mathematical foundations and apply them in graphical application design.

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Course Outcomes

Upon successful completion of this course, students will be able to:

Course Information

• Semester: 4th
• Course Code: 07-0613-CA401
• Credits: 03
• Course Teacher: Md Habibul Basar Faruq
• Email: mh.faruq06@gmail.com

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Course Description

This course introduces the design, analysis, and implementation of algorithms. Students will learn fundamental algorithmic techniques such as divide-and-conquer, greedy strategies, dynamic programming, and graph algorithms. The course emphasizes efficiency analysis using Big-O notation and explores NP-completeness, approximation, and randomized algorithms.

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Course Outcomes

Upon successful completion of this course, students will be able to:

Course Information:

• Semester: 4th
• Course Code: 07-0613-CALab406
• Credits: 01
• Course Teacher: Md Habibul Basar Faruq
• Email: mh.faruq06@gmail.com

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Lab Objectives

Upon completion of this lab, students will be able to:

• Implement fundamental data structures (arrays, linked lists, stacks, queues, trees, graphs)
• Develop and analyze sorting/searching algorithms (selection, insertion, merge, quick, radix)
• Implement recursion-based solutions (Fibonacci, factorial, series summations)
• Apply divide-and-conquer, greedy, and dynamic programming techniques (Knapsack, matrix chain multiplication)
• Explore backtracking (N‑Queens, subset sum) and graph algorithms (DFS, BFS, Dijkstra)

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Weekly Lab Activities

• Week 1: Introduction

Course Information

• Semester: 6th
• Course Code: 07-0619-AI Lab609
• Credits: 01
• Course Teacher: Md Habibul Basar Faruq
• Email: mh.faruq06@gmail.com

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Lab Objectives

Upon completion of this lab, students will be able to:

• Implement fundamental AI algorithms including search, CSP, and Markov Decision Processes
• Develop reinforcement learning agents using Q-learning and policy iteration
• Build and train simple machine learning models including classifiers and regressors
• Construct and train neural networks using backpropagation
• Apply probabilistic inference in Bayesian networks
• Develop AI applications for real-world problems and game environments
• Use AI programming tools and libraries such as Python, TensorFlow, and PyTorch

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Weekly Lab Activities

• Week 1: Lab Setup and Environment

Course Information

• Semester: 6th
• Course Code: 07-0619-AI603
• Credits: 03
• Course Teacher: Md Habibul Basar Faruq
• Email: mh.faruq06@gmail.com

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Course Description

This course provides a comprehensive introduction to Artificial Intelligence, covering foundational concepts, algorithms, and applications. Students will explore problem solving, knowledge representation, reasoning, learning, and perception, integrating theory with hands-on programming assignments.

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Weekly Topics and Resources

• Week 1: AI Overview & Foundations
  Introduction to AI concepts and history.
  Intro Slides (UW CSE573)

• Week 2: Problem Solving & Search
  Uninformed and informed search strategies (DFS, BFS, A*).
  Search Slides
  Informed Search Slides (Annotated)

Course Information

• Semester:
• Course Code:
• Credits:
• Course Teacher: Md Habibul Basar Faruq
• Email: mh.faruq06@gmail.com

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Course Description

This lab course provides hands-on experience in web technologies, focusing on HTML, CSS, JavaScript, and the Django web framework.
Students will learn to design, style, and script web pages, and later integrate them into dynamic, database-driven web applications using Django.

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Weekly Lab Topics and Tasks

• Week 1: Introduction to Markup and Scripting
  Understand markup vs scripting and basic HTML structure.
  Task: Create a simple HTML5 webpage using headings, paragraphs, images, and links.