IGNOU MST 26 SOLVED ASSIGNMENT
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MST 26: Introduction to Machine Learning
| Title Name | IGNOU MST 26 SOLVED ASSIGNMENT |
|---|---|
| Type | Soft Copy (E-Assignment) .pdf |
| University | IGNOU |
| Degree | MASTER DEGREE PROGRAMMES |
| Course Code | MSCAST |
| Course Name | M.Sc. (Applied Statistics) |
| Subject Code | MST 26 |
| Subject Name | Introduction to Machine Learning |
| Year | 2025 |
| Session | - |
| Language | English Medium |
| Assignment Code | MST 26/Assignment-1/2025 |
| Product Description | Assignment of MSCAST (M.Sc. (Applied Statistics)) 2025. Latest MST 026 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
- January 2025 Session: 31st October, 2025
- July 2025 Session: 30th April, 2025
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MST 026 (January 2025 - July 2025) - ENGLISH
TUTOR MARKED ASSIGNMENT
MST-026: Introduction to Machine Learning
Course Code: MST-026
Assignment Code: MST-026/TMA/2025
Maximum Marks: 100
Note: All questions are compulsory. Answer in your own words.
1. Explain each of the following with an example
• Supervised Learning,
• Unsupervised learning,
• Reinforcement Learning,
• Semi-supervised.
2. What is the role of loss function in machine learning algorithms? Explain any two commonly used loss functions in machine learning algorithms.
3. If the input vectors arand initial values of three weight vectors are
then calculate the resulting weight found after training the competitive layer with Kohonen’s rule and a learning rate
of 0.5 on the input-series in order I1,I2,and I3,
4. Consider the following table for the connections between the input neurons and the hidden layer neurons.
| Input neuron | Hidden layer neurons | Connection weithts |
| 1 | 1 | 1 |
| 1 | 2 | 0.1 |
| 1 | 3 | -1 |
| 2 | 1 | 1 |
| 2 | 2 | -1 |
| 2 | 3 | -1 |
| 3 | 1 | 0.2 |
| 3 | 2 | 0.3 |
| 3 | 3 | 0.6 |
The connections weights from Hidden layer neurons to the output neurons are 0.5, 0.3 and 0.6 for first, second and third neurons respectively corresponding threshold value for output layer is 0.5 and for hidden layer 1.8, 0.05 and – 0.2 for first, second and third neuron respectively,
(a) Draw the diagraph of the network.
(b) Write the results of activation and interpret.
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