FREE
AKTU Advanced Machine Learning (BML061) Syllabus PDF – B.Tech 3rd Year
Advanced Machine Learning BML061
All syllabus, notes, question bank & PYQ for this subject
Free
Download now
- English
- Semester 6
- 0 downloads
- 0 views
Official AKTU syllabus of Advanced Machine Learning (BML061) for B.Tech 3rd Year – 3 credits, L-T-P 3-0-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Advanced Machine Learning
Download PDFBML061 · 3 credits · L-T-P 3-0-0
Course Outcomes
At the end of course , the student will be able to:
| CO 1 | Understand advanced concepts and methods of machine learning and to develop an understanding of the role of machine learning in massive scale automation. | K , K 1 2 |
| CO 2 | Apply various machine learning algorithms in a range of real-world applications. | K , K 3 3 |
| CO 3 | Integrate and apply their expertise to produce solutions for real-world problems. | K , K 4 5 |
| CO 4 | Comparative Analysis of different Machine Learning Algorithms | K 4 |
| CO 5 | Interpret and Analyze results with reasoning using different ML techniques. | K , K 4 5 |
Course Content
L-T-P: 3-0-0
| Unit | Topic | Proposed Lecture |
|---|---|---|
| I | Artificial Neural Network Introduction to ANN, Perceptron, Cost Function, Gradient Checking, multi-layer perceptron and backpropagation algorithm that is used to help learn parameters for a neural network, Random Initialization | 08 |
| II | Bayesian Learning Probability theory and Bayes rule, Naive Bayes learning algorithm, Bayes nets. | 08 |
| III | Decision Trees Representing concepts as decision trees, Recursive induction of decision trees, best splitting attribute: entropy and information gain. Searching for simple trees and computational complexity, Overfitting, noisy data, and pruning. | 08 |
| IV | Reinforcement Learning Reinforcement earning through feedback network, function approximation. | 08 |
| V | Ensemble Methods Bagging, boosting, stacking and learning with ensembles. Random Forest | 08 |
Text Books
- Tom Mitchell, Machine Learning, McGraw Hill, 1997.
- Jeeva Jose, Introduction to Machine Learning, Khanna Book Publishing 2020.
- Rajiv Chopra, Machine Learning, Khanna Book Publishing 2021
- EthemApaydin, Introduction to Machine Learning, 2e. The MIT Press, 2010.
- Kevin P. Murphy, Machine Learning: a Probabilistic Perspective, The MIT Press, 2012.
Ratings & Reviews
No reviews yet — share your experience with this item.