IGNOU MCSL 69 SOLVED ASSIGNMENT
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MCSL 69: Artificial Intelligence and Machine Learning Lab
| Title Name | IGNOU MCSL 69 SOLVED ASSIGNMENT |
|---|---|
| Type | Soft Copy (E-Assignment) .pdf |
| University | IGNOU |
| Degree | MASTER DEGREE PROGRAMMES |
| Course Code | MSCDSA |
| Course Name | Master of Science (M.Sc.) (Data Science and Analytics) (ODL) |
| Subject Code | MCSL 69 |
| Subject Name | Artificial Intelligence and Machine Learning Lab |
| Year | 2026 2027 |
| Session | - |
| Language | English Medium |
| Assignment Code | MCSL 69/Assignment-1/2026 2027 |
| Product Description | Assignment of MSCDSA (Master of Science (M.Sc.) (Data Science and Analytics) (ODL)) 2026 2027. Latest MCSL 069 2026 Solved Assignment Solutions |
| Last Date of IGNOU Assignment Submission | Last Date of Submission of IGNOU BEGC-131 (BAG) 2025-26 Assignment is for January 2026 Session: 30th September, 2026 (for December 2025 Term End Exam). Semester Wise January 2025 Session: 30th March, 2026 (for June 2026 Term End Exam). July 2025 Session: 30th September, 2025 (for December 2025 Term End Exam). |
| Format | Ready-to-Print PDF (.soft copy) |
📅 Important Submission Dates
- July 2026 Session: 30th April, 2027
- January 2027 Session: 31st October, 2026
Why Choose Our Solved Assignments?
• Guidelines: Strictly follows 2025-26 official word limits.
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📋 Assignment Content Preview
MCSL 069 (July 2026 - January 2027) - ENGLISH
Course Code : MCSL-069
Course Title : Artificial Intelligence & Machine Learning Lab
Assignment Number : MSCDSA(II)/L-069/Lab_Assign/2026-27
Maximum Marks : 100
Weightage : 30%
Last Date of Submission : 31st October, 2026 (for July session)
30th April, 2027 (for January session)
This assignment has 8 Questions. Answer all the questions. The total marks for all the
questions are 40, and the maximum marks for each question are mentioned. Your Lab
Records will carry 40 Marks. The remaining 20 marks are for viva voce. You may use
illustrations and diagrams to enhance the explanations. Please go through the guidelines
regarding assignments given in the programme guide for the format of presentation.
Note: You must execute the program and submit the program logic, sample input, and output along
with the necessary documentation. Assumptions can be made wherever necessary.
Q1: Write a Python Program to solve the N-Queen Problem without using Recursion.
Q2: Implement the Water Jug problem in Python.
Q3: Write a Python Program to implement the Min-Max Algorithm.
Q4: Write a Python Program to implement the AO* Algorithm
Q5: Discuss the Naïve Bayes algorithm and write the Python code to demonstrate the execution of the
Naïve Bayes algorithm on the dataset of your choice.
Q6: Write a Python Program to implement Logistic Regression for data classification, and choose a dataset
of your own choice.
Q7: Take a real-time example to implement the ID3 decision tree classification algorithm in Python.
Q8: Write a Python Program to implement Support Vector Machines for data classification , and choose a
dataset of your own choice.
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