Computer Engineering Archives - Student Notes

AI, ML, and DL: Core Concepts and Relationships

Posted by admin on Mar 17, 2026 in Computer Engineering | 0 comments

AI, ML, and DL: Core Concepts

Correlation: AI, ML, and DL

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are interconnected fields, each building upon the other, but they are not synonymous. AI is the broader concept of machines being able to carry out tasks in a way that we would consider “intelligent.” ML is a subset of AI that focuses on the development of algorithms that allow computers to learn from and make predictions or decisions based on data. DL is a subfield

AI, Machine Learning, and Deep Learning: Core Concepts

Posted by admin on Mar 16, 2026 in Computer Engineering | 0 comments

The Relationship Between AI, ML, and DL

The correlation between these three fields is best understood as a hierarchical relationship where each is a sub-field of the previous one:

Artificial Intelligence (AI): The broad field of creating systems capable of performing tasks that typically require human intelligence (e.g., reasoning and problem-solving).
Machine Learning (ML): A subset of AI that focuses on the use of algorithms and statistical models to allow computers to learn from data without being

Python Data Science Reference: Pandas, NumPy, and Files

Posted by admin on Mar 15, 2026 in Computer Engineering | 0 comments

Python String and List Operations

Use s[start(in):stop(ex):step] for slicing. For string methods in Pandas, ensure you use .str.(strfunction).

s.upper() #P
s.lower() #s
s.title() #Py
s.find("P") #0
s.replace("old", "new", "count")
s.strip()
s.startswith()
s.endswith()
s.split(sep, maxsplit)
s.join(parts)
s.count(sub, start, end)

List Methods and Comprehensions

l.append(x)
l.clear()
l.copy()
l.count(x)
l.sort()
l.insert(1, "a")
l.pop(x)
l.remove(x)

List Comprehensions:

l = [expression for item in iterable]
l = [expression

Algorithm Analysis and Complexity: A Comprehensive Reference

Posted by admin on Mar 11, 2026 in Computer Engineering | 0 comments

Asymptotic Notations

1. Big-O Notation (Upper Bound)

Definition: Big-O gives the maximum growth rate of a function, representing the worst-case complexity.

Formula: f(n) = O(g(n)) where 0 ≤ f(n) < c · g(n) for all n ≥ n₀
Meaning: Function f(n) does not grow faster than g(n).
Example: 3n² + 5n + 2 = O(n²)

2. Big-Omega Notation (Lower Bound)

Definition: Big-Omega gives the minimum growth rate, representing the best-case complexity.

Formula: f(n) = Ω(g(n)) where 0 ≤ c · g(n) ≤ f(n) for all

Artificial Intelligence Search Algorithms and CSP Methods

Posted by admin on Mar 10, 2026 in Computer Engineering | 0 comments

Search Problem Components

State Space: All possible configurations.
Initial State: Starting configuration.
Successor Function: Allowed transitions between states.
Goal Test: Checks if the state satisfies the objective.
Cost Function (Optional): Cost per action.
Heuristic Function (Optional): Estimate to direct search.
Solution: Sequence of actions from initial to goal state.
State Space Graph: Vertices are states, edges are transitions.
Search Tree: Tree of paths explored by the algorithm.
Node: Data structure

Essential Algorithms and Complexity Theory Reference