Welcome to my blog! This is where my journey to start recording the world of programming begins 🎉
In this blog, I will document my course learning records, showcase my projects, and share my personal reflections on my experiences in programming. My aim is to create a space for learning and sharing knowledge with fellow enthusiasts.
Master of Information Technology, Specializing in Computing
The University of Melbourne
Feb 2024 - Dec 2025
Relevant Courses: Programming and Software Development, Internet Technology, Algorithms and Complexity, Database Systems and Information Modelling, Distributed Systems, Software Process and Management, Machine Learning, Cluster and Cloud Computing, Declarative Programming, Information Visualization
Purpose: Code coverage analysis tool
Measures how much of your source code is exercised by your unit tests
Tracks:
Generates visual HTML reports showing what parts of code are untested
| Algorithm | Type | Generative/Discriminative | Linear/Non-linear | Notes |
|---|---|---|---|---|
| Naive Bayes | Classification | Generative | Linear | Assumes feature independence, uses Bayes' theorem |
| Logistic Regression | Classification | Discriminative | Linear | Uses sigmoid function, mainly for binary classification |
| Decision Trees | Classification | Discriminative | Non-linear | Handles both numerical and categorical features |
| SVM | Classification | Discriminative | Linear/Non-linear | Uses kernel trick for non-linear, finds optimal hyperplane |
| k-NN | Classification | Discriminative | Non-linear | Instance-based, sensitive to feature scaling |
| Neural Networks | Classification | Discriminative | Non-linear | Can model complex patterns, requires large datasets |
| Linear Regression | Regression | Discriminative | Linear | Predicts continuous values, sensitive to multicollinearity |
| k-Means | Clustering | - | Linear | Unsupervised, sensitive to initial centroids and outliers |
| PCA | Dimensionality Reduction | - | Linear | Unsupervised, maximizes variance, does not consider class labels |
| Hierarchical Clustering | Clustering | - | Non-linear | Unsupervised, builds nested clusters, linkage methods affect results |
| Perceptron | Classification | Discriminative | Linear | Simple model, only for linearly separable data |
| Multi-Layer Perceptron | Classification/Regression | Discriminative | Non-linear | Can solve XOR problem, uses backpropagation for training |
# server.py
from mcp.server.fastmcp import FastMCP
# Create an MCP server
mcp = FastMCP("Demo")
# Add an addition tool
@mcp.tool()
def add(a: int, b: int) -> int:
"""Add two numbers"""
return a + b
# Add a dynamic greeting resource
@mcp.resource("greeting://{name}")
def get_greeting(name: str) -> str:
"""Get a personalized greeting"""
return f"Hello, {name}!"
if __name__ == "__main__":
mcp.run(transport="stdio")A digital platform typically used for enterprise digital transformation, integrating various business management and data analysis tools to improve operational efficiency.
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Depth-First Traversal Preorder Traversal (Recursive, Iterative) Left-Right Inorder Traversal (Recursive, Iterative) Left-Middle-Right Postorder Traversal (Recursive, Iterative) Left-Right-Middle Breadth-First Traversal Level Order Traversal (Iterative)