MCSL-069 IGNOU Solved Assignment 2026-27

MCSL-069 Solved Assignment: Artificial Intelligence & Machine Learning Lab (TMA)

This structured guide offers 100% verified solutions for the TMA of MCSL-069, designed to meet the 30% course weightage requirement. All responses are compliant with IGNOU’s academic integrity policies, including word limits and plagiarism checks.

Key Syllabus Units Covered

• Unit 1: Introduction to AI & ML

  Covers foundational concepts like problem-solving techniques, search algorithms (BFS/DFS), and heuristic search methods. Solutions include Python implementations of astar.py for pathfinding and theoretical justifications for algorithmic complexity.

• Unit 2: Knowledge Representation & Reasoning

  Focuses on predicate logic, first-order reasoning, and rule-based systems. Sample solutions demonstrate Prolog queries for family relationships and truth maintenance systems (TMS) in Python, with step-by-step explanations of inference rules.

• Unit 3: Machine Learning Fundamentals

  Includes supervised/unsupervised learning paradigms, k-NN classification, and decision trees. Provides step-by-step code for scikit-learn implementations (e.g., KNeighborsClassifier) and validation metrics (accuracy/precision/recall) with real-world datasets.

• Unit 4: Neural Networks & Deep Learning

  Explains perceptrons, backpropagation, and feedforward networks. Solutions include TensorFlow/Keras code snippets for MNIST digit classification, with hyperparameter tuning (learning rate, epochs) and model evaluation plots.

• Unit 5: Natural Language Processing (NLP) Basics

  Covers tokenization, stemming, and Naive Bayes for text classification. Provides NLTK-based solutions for sentiment analysis on movie reviews, with preprocessing pipelines and confusion matrix visualizations.

Assignment Structure & Submission Tips

The TMA consists of 5 practical questions (Python-based) and 3 theoretical questions, requiring:

• Code snippets (Python 3.x) with comments for clarity.
• Output screenshots (if applicable) for visualization tasks.
• Mathematical derivations for theoretical parts (e.g., time complexity of algorithms).

FAQs

• Q: Are solutions provided in English or Hindi?

  Solutions are available in both English and Hindi, formatted as per IGNOU’s official guidelines. The TMA must be submitted in the preferred language (as per the student’s choice).

• Q: How do I ensure compliance with word limits?

  Each answer is structured with bullet points for conciseness and code blocks for programs, avoiding redundant explanations. The provided solutions strictly adhere to the 500/250/100-word limits per question.

Submission Deadlines & Format

The TMA must be submitted within the IGNOU academic session deadline (typically 6 months from the study center’s assignment release date). The format requires:

• A PDF file (single document) with:
  • Question numbers clearly labeled.
  • Python code in monospace font.
  • Outputs/images embedded (if required).
• No manual signatures; use digital submission via IGNOU’s online portal.