Machine Learning & Neural Networks detailed syllabus for Artificial Intelligence & Machine Learning (AM) for 2021 revision curriculum has been taken from the SITTTRs official website and presented for the Artificial Intelligence & Machine Learning students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.
For Artificial Intelligence & Machine Learning 4th Sem scheme and its subjects, do visit Artificial Intelligence & Machine Learning (AM) 4th Sem 2021 revision scheme. The detailed syllabus of machine learning & neural networks is as follows.
Course Objectives:
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Course Outcomes:
On completion of the course, the students will be able to:
- Demonstrate supervised machine learning techniques
- Demonstrate clustering and dimensionality reduction techniques
- Demonstrate evaluation metrics for machine learning models
- Demonstrate the concept of Artificial Neural Networks
Module 1:
Machine Learning – Definition -Applications – Processes involved in Machine Learning, Machine Learning Techniques-Supervised Learning, Unsupervised Learning and Reinforcement Learning, Data preprocessing – Normalization, Missing value handling. Supervised Learning- Classification – Learning a Class from Examples, Logistic Regression – K-Nearest Neighbours – Support Vector Machine – Naive Bayes – Decision Tree – Random Forest Regression- Linear Regression -Multi Linear Regression
Module 2:
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Module 3:
Reinforcement learning: Key features- types – Markov Decision Process-Q learning Evaluation Measures: Regression: Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, R-squared. Classification: Accuracy, confusion matrix, precision, recall, F-Score, ROC-Curve. Clustering: Sum of Squared Error, Silhouette Coefficient, Dunn’s Index, Ensemble methods, Bootstrapping, Cross Validation.
Module 4:
Artificial Neural Network: Neural network representation-Structure-Advantages, Perceptron, Training a perceptron, Multilayer perceptron, Back-propagation Algorithm. Recurrent Neural Networks. Loss function, Activation function, Batch normalization.
Text Books:
For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier..
Online Resources
- https://www.javatpoint.com/machine-learning-models
- Introduction to Machine Learning – Course (nptel.ac.in)
For detailed syllabus of all other subjects of Artificial Intelligence & Machine Learning (AM), 2021 revision curriculum do visit Artificial Intelligence & Machine Learning 4th Sem subject syllabuses for 2021 revision.
To see the syllabus of all other branches of diploma 2021 revision curriculum do visit SITTTR diploma all branches syllabus..
To see the results of Artificial Intelligence & Machine Learning (AM) of diploma 2021 revision curriculum do visit SITTTR diploma Artificial Intelligence & Machine Learning (AM) results..
For all Artificial Intelligence & Machine Learning academic calendars, visit Artificial Intelligence & Machine Learning all semesters academic calendar direct link.