AI Practical Kit

A Python package that prints code snippets for the eight Artificial Intelligence practicals in the supplied BSc IT practical syllabus.

Install locally

python -m pip install .

Usage

from ai_practicals import p1, p2, p5, list_practicals, list_questions, search_practicals

p1()
p1(2)
p5(3)
list_practicals()
list_questions(5)
search_practicals("Bayes")

Each pN(question=1) prints the code for that question and also returns the code as a string. Use p1(2) for Practical 1, Question 2.

Available practicals

• Practical 1: Table-driven agent and 8-puzzle BFS
• Practical 2: Water jug DFS and UCS
• Practical 3: Greedy Best-First 8-puzzle
• Practical 4: A* 8-puzzle and weighted graph
• Practical 5: Bayes' theorem, Naive Bayes, Bayesian Network
• Practical 6: ML workflow, Linear Regression, MNIST binary classification
• Practical 7: K-fold, Decision Tree classification, Decision Tree regression
• Practical 8: kNN and multiclass Logistic Regression

Optional dependencies

The code snippets are returned as text and do not require scikit-learn or pgmpy just to import the package. To execute a particular snippet, install the packages imported by that snippet. The MNIST example downloads data from OpenML and needs an internet connection on first run.