AISD — AI & Software Development Course Materials

📚 Complete study materials for Algonquin College AI & Software Development (AISD) program Including lecture notes, lab solutions, interactive review hubs, practice quizzes, cheat sheets, math foundations, and more.

🇨🇳 Algonquin College AI 与软件开发课程学习资料全集 — 包含讲义笔记、实验代码、交互式复习平台、练习题库、速查表、数学基础等。

✨ Highlights / 亮点

Feature	Description
🎯 6 Complete Courses	ML, MV, NLP, RL, Math Foundations, Capstone Project
📝 Interactive Review Hubs	Self-hosted HTML quiz apps with instant feedback for each course
🧮 Math Foundations	16 standalone math prereq documents with textbook citations
📊 100+ Practice Quiz Questions	JSON-based quizzes covering midterms, finals, and weekly topics
📖 Week-by-Week Storylines	Narrative lecture notes explaining why before how
🔬 Lab Code & Solutions	Jupyter notebooks and Python scripts for all lab assignments
📋 Exam Cheat Sheets	Condensed review sheets optimized for open-book exams

📂 Project Structure / 项目结构

aisd/
├── courses/
│   ├── ml/           # Machine Learning (机器学习) — CST8507
│   ├── mv/           # Machine Vision (机器视觉) — CST8508
│   ├── nlp/          # Natural Language Processing (自然语言处理) — CST8507
│   ├── rl/           # Reinforcement Learning (强化学习) — CST8509
│   ├── pj/           # Capstone Project (毕业设计) — CST8510
│   ├── math/         # Math Foundations (数学基础) — cross-course
│   └── fuse/         # FUSE Challenge (跨学科创客挑战赛)
├── knowledge-map/    # AI knowledge maps & concept registry
├── textbooks/        # Reference textbook PDFs
├── scripts/          # Utility scripts (PDF processing, search, etc.)
└── pyproject.toml    # Python dependencies (uv managed)

📘 Course Details / 课程详情

Machine Learning (ML) — CST8507

机器学习：从数据预处理到集成学习的完整课程

Topics covered:

Data Preprocessing (数据预处理) — normalization, missing values, feature scaling
Support Vector Machines (SVM, 支持向量机) — kernel trick, margin optimization
Convolutional Neural Networks (CNN, 卷积神经网络) — architecture, backpropagation
Recurrent Neural Networks (RNN, 循环神经网络) — LSTM, sequence modeling
Naive Bayes Classifier (朴素贝叶斯) — Gaussian, Multinomial, Bernoulli
Clustering (聚类) — K-Means, EM algorithm, Gaussian Mixture Models
Imbalanced Class Problem (类别不平衡) — SMOTE, cost-sensitive learning
Classifier Fusion (分类器融合) — Bagging, Boosting, Stacking
Association Rule Mining (关联规则挖掘) — Apriori, support/confidence/lift

Materials include:

📊 Weekly review notes (Week 1–11)
📝 Interactive Review Hub (courses/ml/review/index.html)
🧮 Calculation problem sets with worked solutions
📋 Quiz bank: midterm + final + weekly quizzes (JSON)
📖 Week-by-week storyline narratives

Machine Vision (MV) — CST8508

机器视觉：从图像处理基础到深度学习目标检测

Topics covered:

Image Processing Fundamentals (图像处理基础) — filters, convolution, edge detection
Feature Detection & Matching (特征检测与匹配) — SIFT, ORB, FAST
CNN for Vision (视觉CNN) — LeNet, AlexNet, VGG, ResNet
Deep Learning Frameworks (深度学习框架) — PyTorch, TensorFlow
Object Detection (目标检测) — YOLO, R-CNN, SSD
Object Tracking (目标跟踪) — optical flow, Kalman filter
Sensor Fusion (传感器融合) — LiDAR + Camera, multi-modal
OpenMMLab Ecosystem (OpenMMLab 生态) — MMPretrain, MMDetection

Materials include:

🔬 5 complete lab solutions with Jupyter notebooks (OpenCV, ORB, PyTorch)
📝 Interactive Review Hub (courses/mv/review/index.html)
📋 Quiz bank: midterm + final + weekly quizzes (JSON)
📖 11-week storyline narratives with slide summaries
🎤 Audio storyline narrations for review

Natural Language Processing (NLP) — CST8507

自然语言处理：从文本预处理到大语言模型

Topics covered:

Text Preprocessing (文本预处理) — tokenization, stemming, lemmatization
Word Embeddings (词嵌入) — Word2Vec, GloVe, FastText
Language Models (语言模型) — N-gram, neural LMs
Sequence Models (序列模型) — RNN, LSTM, GRU, Seq2Seq
Attention Mechanisms (注意力机制) — self-attention, multi-head
Transformer Architecture (Transformer 架构) — encoder-decoder, positional encoding
Pre-trained Models (预训练模型) — BERT, GPT, T5
NLP Applications (NLP 应用) — NER, sentiment analysis, machine translation

Materials include:

📝 13-lecture storyline narratives and slide notes
📋 Comprehensive cheat sheets per topic (12 files)
📊 Unified cheat sheet (HTML + Markdown)
📋 Quiz bank: weekly quizzes + final short answer (JSON)
🔬 Demo scripts with Python implementations

Reinforcement Learning (RL) — CST8509

强化学习：从 MDP 到 DQN，从 Gymnasium 到 Gazebo

Topics covered:

RL Fundamentals (强化学习基础) — agent, environment, reward, policy
Markov Decision Process (MDP, 马尔可夫决策过程) — Bellman equation, discount factor
OpenAI Gymnasium (Gymnasium 环境) — CartPole, FrozenLake, custom envs
Stable-Baselines3 (SB3) — PPO, A2C, DQN training
Deep Q-Network (DQN, 深度Q网络) — experience replay, target network
Dynamic Programming (动态规划) — value iteration, policy iteration
Monte Carlo Methods (蒙特卡洛方法) — first-visit, every-visit MC
Value Function Approximation (值函数近似) — linear, neural network
Gazebo + ROS (机器人仿真) — robotic RL simulation
Docker for RL (容器化部署) — containerized training environments

Materials include:

🔬 5 lab reports (CliffWalking, Gymnasium, Gazebo, Actor-Critic, Docker)
📝 Interactive Review Hub (courses/rl/review/index.html)
📋 Quiz bank: 11 quiz files including midterm + final (JSON)
📖 10-week storyline narratives
📋 Final exam cheat sheet
🔬 Complete merged RL notes (586K+ words)

Math Foundations (数学基础)

跨课程数学前置知识，每个公式都有教科书出处

Topics covered:

Linear Algebra — vectors, matrices, inner product, norms, eigenvalues, SVD
Calculus — derivatives, chain rule, gradients, geometric series
Probability — conditional probability, Bayes' theorem, Markov chains, cross entropy
Statistics — mean, variance, Gaussian distribution, MLE
Optimization — gradient descent, Lagrange multipliers
General — argmax, convolution

16 standalone documents with dependency maps and course reading lists. Each formula includes citations from: Mathematics for Machine Learning, Bayesian Reasoning and ML, Deep Learning (Goodfellow), Convex Optimization (Boyd), Reinforcement Learning (Sutton & Barto).

Capstone Project (PJ) — CST8510

毕业设计：Ottawa Economic Development RAG System

A RAG (Retrieval-Augmented Generation) system for Ottawa economic development data.

Materials include:

📊 Final presentation slides
📄 Final report (Word)
📋 Quiz materials and review notes
🔗 Reference GitHub repositories

🎮 Interactive Review Hubs / 交互式复习平台

Each course has a self-contained HTML review hub that runs locally in your browser — no server needed.

# Open any review hub directly in your browser:
start courses/rl/review/index.html    # Windows
open courses/mv/review/index.html     # macOS

Features:

✅ Multiple choice, true/false, and short answer quizzes
✅ Instant answer reveal with explanations
✅ LaTeX formula rendering (MathJax)
✅ Score tracking per quiz
✅ Storyline and cheat sheet tabs for narrative review

🧮 Quiz Data Format / 题库数据格式

All quizzes use a standardized JSON format, making them easy to extend or port to other tools:

{
  "title": "Quiz Title",
  "questions": [
    {
      "id": 1,
      "type": "multiple_choice",
      "question": "What is the Bellman equation?",
      "options": ["A. ...", "B. ...", "C. ...", "D. ..."],
      "answer": "B",
      "explanation": "The Bellman equation expresses..."
    }
  ]
}

🚀 Getting Started / 快速开始

Prerequisites

Python ≥ 3.10
uv (recommended package manager)
CUDA-compatible GPU (optional, for DL/RL training)

Setup

# Clone the repository
git clone https://github.com/<your-username>/aisd.git
cd aisd

# Install all dependencies with uv
uv sync

# Activate virtual environment
.venv\Scripts\activate     # Windows
source .venv/bin/activate  # macOS / Linux

# Or run scripts directly without activation
uv run python courses/mv/labs/lab3_orb.py

Quick Start — Review for Exams

# 1. Open an interactive review hub in your browser
start courses/ml/review/index.html

# 2. Read a storyline narrative
cat courses/rl/notes/week1_rl_intro_storyline.md

# 3. Check math prerequisites
cat courses/math/README.md

🛠️ Utility Scripts / 工具脚本

Script	Purpose
`scripts/batch_mineru.py`	Batch convert PDFs to Markdown (MinerU)
`scripts/build_vectors.py`	Build vector database for textbook search
`scripts/rebuild_db.py`	Rebuild ChromaDB for semantic search
`scripts/rebuild_toc.py`	Generate table of contents from PDFs
`scripts/search_github_books.py`	Search GitHub for open textbooks

📐 Tech Stack / 技术栈

Category	Tools
Package Manager	uv
ML/DL	NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch
Computer Vision	OpenCV, MMPretrain, MMDetection
NLP	NLTK, Gensim, HuggingFace Transformers
RL	Gymnasium, Stable-Baselines3
Document Processing	PyMuPDF, pdfplumber, python-docx, MinerU
Search	ChromaDB, Sentence-Transformers, BM25

🎓 Who Is This For? / 适合谁?

🧑‍🎓 Algonquin College AISD students — current and future cohorts
📚 Self-learners studying ML, CV, NLP, or RL independently
🔍 Anyone preparing for AI/ML exams — practice quizzes with explanations
🌏 Chinese-speaking learners — bilingual notes and storylines (中英双语笔记)
🤖 AI practitioners — reference implementations and math foundations

📌 Course Codes Reference / 课程代号

Code	Course	Semester
CST8507	Machine Learning / NLP	Winter 2026
CST8508	Machine Vision	Winter 2026
CST8509	Reinforcement Learning	Winter 2026
CST8510	Capstone Project	Winter 2026

🤝 Contributing / 贡献

If you're a fellow student or alumni, feel free to:

🐛 Report issues or typos
📝 Add quiz questions or improve explanations
📖 Contribute storyline notes for missing weeks
🌐 Translate content to other languages

📜 License / 许可

This repository is for educational purposes only. Course slides and official materials remain the property of Algonquin College and their respective instructors. Student-created notes, code, and review materials are shared under MIT License.

🔑 Keywords / 关键词

Algonquin College · AISD · AI Software Development · Machine Learning · Machine Vision · Computer Vision · NLP · Natural Language Processing · Reinforcement Learning · Deep Learning · OpenCV · PyTorch · TensorFlow · Gymnasium · Stable-Baselines3 · DQN · SVM · CNN · RNN · LSTM · Transformer · BERT · GPT · Naive Bayes · K-Means · Clustering · Object Detection · YOLO · Gazebo · ROS · Docker · RAG · study notes · exam review · cheat sheet · practice quiz · course materials · 学习笔记 · 复习资料 · 速查表 · 练习题

Name	education-skill-training
Description	专业的技能培训(Skill Training)顾问助手，帮助您找到合适的技能培训课程。当用户询问以下问题时使用：(1) 学校选择和申请 (2) 课程规划 (3) 学习资源推荐 (4) 认证和评估 (5) 常见问题解答

education-skill-training

SKILL.md

AISD — AI & Software Development Course Materials

✨ Highlights / 亮点

📂 Project Structure / 项目结构

📘 Course Details / 课程详情

Machine Learning (ML) — CST8507

Machine Vision (MV) — CST8508

Natural Language Processing (NLP) — CST8507

Reinforcement Learning (RL) — CST8509

Math Foundations (数学基础)

Capstone Project (PJ) — CST8510

🎮 Interactive Review Hubs / 交互式复习平台

🧮 Quiz Data Format / 题库数据格式

🚀 Getting Started / 快速开始

Prerequisites

Setup

Quick Start — Review for Exams

🛠️ Utility Scripts / 工具脚本

📐 Tech Stack / 技术栈

🎓 Who Is This For? / 适合谁?

📌 Course Codes Reference / 课程代号

🤝 Contributing / 贡献

📜 License / 许可

🔑 Keywords / 关键词