Machine Learning detailed syllabus for Information Technology (IT) for 2019 regulation curriculum has been taken from the Anna Universities official website and presented for the IT 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 Information Technology 8th Sem scheme and its subjects, do visit IT 8th Sem 2019 regulation scheme. For Professional Elective-VII scheme and its subjects refer to IT Professional Elective-VII syllabus scheme. The detailed syllabus of machine learning is as follows.
Course Objective:
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.
.
Unit II
Introduction
Machine Learning – Types of Machine Learning – Supervised Learning – Unsupervised Learning – Basic Concepts in Machine Learning – Machine Learning Process – Weight Space – Testing Machine Learning Algorithms – A Brief Review of Probability Theory -Turning Data into Probabilities – The Bias-Variance Tradeoff.
Suggested Activities:
- Flipped classroom on Artificial Intelligence and Expert Systems.
- Practical – Installing Python and exploring the packages required for machine learning including numpy, scikit-learn, and matplotlib, IPython hmmpytk and pgmpy.
Suggested Evaluation Methods:
- Assignments on different types of learnings.
- Tutorials on probability theory.
Unit II
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..
Unit III
Unsupervised Learning
Mixture Models and EM – K-Means Clustering – Dirichlet Process Mixture Models – Spectral Clustering – Hierarchical Clustering – The Curse of Dimensionality – Dimensionality Reduction – Principal Component Analysis – Latent Variable Models(LVM) – Latent Dirichlet Allocation (LDA).
Suggested Activities:
- Flipped classroom on mixture models.
- External learning – Improving performance of the model using kernel methods.
Suggested Evaluation Methods:
- Assignments on mixture models.
Unit IV
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..
Unit V
Advanced Learning
Reinforcement Learning – Representation Learning – Neural Networks – Active Learning -Ensemble Learning – Bootstrap Aggregation – Boosting – Gradient Boosting Machines -Deep Learning.
Suggested Activities:
- Flipped classroom on neural networks.
- Practical – Implement bagging approach for credit card analysis.
- External learning – Deep networks.
Suggested Evaluation Methods:
- Evaluation of the practical implementation.
- Assignments on deep networks.
Course Outcome:
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..
Text Books:
- Ethem Alpaydin, “Introduction to Machine Learning”, Third Edition, Prentice Hall of India, 2015.
References:
- Christopher Bishop, “Pattern Recognition and Machine Learning”, Springer, 2006.
- Kevin P. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2012.
- Stephen Marsland, “Machine Learning – An Algorithmic Perspective”, Second Edition, CRC Press, 2014.
- Tom Mitchell, “Machine Learning”, McGraw-Hill, 2017.
- Trevor Hastie, Robert Tibshirani, Jerome Friedman, “The Elements of Statistical Learning”, Second Edition, Springer, 2008.
- Fabio Nelli, “Python Data Analytics with Pandas, Numpy, and Matplotlib”, Second Edition, Apress, 2018.
For detailed syllabus of all the other subjects of Information Technology 8th Sem, visit IT 8th Sem subject syllabuses for 2019 regulation.
For all Information Technology results, visit Anna University IT all semester results direct link.