AI, Machine Learning, and Deep Learning: Core Concepts
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
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()#Ps.lower()#ss.title()#Pys.find("P")#0s.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
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
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
Matrix Chain Multiplication
- Begin
- If N = 1: Print “Cost = 0” and Exit.
- i = 0 (Start index for splitting).
- Repeat steps 5 & 6 while i < N – 1:
- If i < N – 1:
- Cost1 = MCM(P, i + 1)
- Cost2 = MCM(P + i + 1, N – i – 1)
- CurrentCost = Cost1 + Cost2 + (P[0] * P[i + 1] * P[N])
- If i = 0: MinCost = CurrentCost; Else if CurrentCost < MinCost: MinCost = CurrentCost
- i = i + 1
- Print “Minimum cost = “, MinCost
- Exit
Job Sequencing with Deadline (Greedy)
- Start
- Sort all jobs in descending order of profit.
- Find MaxDeadline
Core Concepts of CAD Modeling and Geometric Representation
Bezier Surfaces
A Bezier surface is a parametric surface used in computer-aided design for modeling smooth and curved shapes. It is an extension of the Bezier curve into two parameters, generally represented by u and v. A Bezier surface is defined by a grid of control points that influence the shape of the surface. The surface does not necessarily pass through all control points, but its shape is controlled by them.
Bezier surfaces use Bernstein polynomials for mathematical representation. They provide
Read More