Software Verification and Validation (SEN 304)
38
Possible Question Sources
My Notes
Download PDF
Download ePub
Notes
University Notes (Feb 2026 - Jun 2026)
1
Parallel Programming Section
Parallel Programming (CSC304)
2
Chapter 1: Why Parallel Computing?
3
Chapter 2: Parallel Hardware and Parallel Software
4
Chapter 3: Distributed Memory Programming with MPI
5
CUDA Programming
Simulation & Modeling (CSC305)
6
Simulation & Modeling: Introduction
7
Single-Channel Queue Simulation
8
Simulation & Modeling Midterm 2025
9
Random Number Generation
10
Simulation and Modeling Final 2025
11
Final Exam Cheatsheet
Human Computer Interaction (SEN303)
12
Notes for Studying HCI
13
HCI Midterm 2025
14
Midterm Cheatsheet
15
Lecture 1: Introduction to Human-Computer Interaction
16
Introduction to Human-Computer Interaction (Part 1)
17
Introduction to Human-Computer Interaction (Part 2)
18
Understanding and Conceptualizing Interaction (Part 1)
19
Understanding and Conceptualizing Interaction (Part 1)
20
Lecture 3: Understanding Users
21
Lecture 4: Social Aspects of HCI
22
Final 2023 Answers
23
HCI Cheatsheet
Software Requirements Engineering (SEN302)
24
Lecture 1: Introduction
25
Lecture 2: Introduction
26
Lecture 3: Software Requirements Specification
27
Lecture 4: Elicitation
28
Lectures 5 & 6: Analysis Modeling
29
Lecture 7: Requirements Validation and Negotiation
30
SRE Revision Questions
31
SRE Sample Online Questions
Software Verification and Validation (SEN 304)
32
Chapter 1: Importance of Software Testing
33
Chapter 2: Software Testing Types and Techniques
34
Chapter 3: SDLC
35
Chapter 4: Test Planning
36
Chapter 5: Test Design Techniques
37
Chapter 6: Test Execution
38
Possible Question Sources
Advanced Database
39
Ebook Excercises Solutions
40
Final Exam Answers
Internships & Opportunities
41
Google Summer of Code 2026
42
Hong Kong University Internship
43
Nile University Research Internship 2026
Papers
44
From Learning Models of Natural Image Patches to Whole Image Restoration
45
Natural Images, Gaussian Mixtures, and Dead Leaves
46
Deep Image Prior
47
Variational Inference with Normalizing Flows
48
Glow: Generative Flows with Invertible 1x1 Convolutions
49
Invertible Residual Networks
50
Modeling High-Dimensional Discrete Data with Multi-Layer Neural Networks
51
Pixel Recurrent Neural Networks
52
Improved Variational Inference with Inverse Autoregressive Flow
53
Language Model Beats Diffusion – Tokenizer is Key to Visual Generation
54
Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
55
An Image is Worth 32 Tokens for Reconstruction and Generation
56
Autoregressive Image Generation without Vector Quantization
57
Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model
Stanford Deep Generative Models Course
58
Introduction
59
Background
60
Autoregressive Models
61
Maximum Likelihood Learning
62
Variational Autoencoders (VAE)
Stanford Machine Learning with Graphs
63
Graph Neural Networks
IELTS
64
General Notes and Tips
65
Liz IELTS Writing Task 1
66
Common Mistakes
ICLR 2026
67
ICLR 2026 Notes
68
ICLR 2026 Papers
References
Software Verification and Validation (SEN 304)
38
Possible Question Sources
38
Possible Question Sources
https://www.scribd.com/document/811865455/Imoprtant-MCQ-for-Modeling-and-simulation-1
https://wayground.com/admin/quiz/67bfc5c9d6226d934aa3fb96/itmc-quiz-1
https://www.cliffsnotes.com/study-notes/29080748
37
Chapter 6: Test Execution
39
Ebook Excercises Solutions