Machine Learning detailed syllabus for Master of Computer Applications (MCA), 2018 regulation has been taken from the University of Mumbai official website and presented for the MCA students. For Scheme related information like Course Code, Course Title, Test 1, Test 2, Avg, End Sem Exam, Team Work, Practical, Oral, Total, and other information, do visit full semester subjects post given below.
For Master of Computer Applications (MCA) 5th Sem scheme, 2018 regulation, do visit MCA 5th Sem 2018 Pattern Scheme. For the Elective-1 (Departmental Level) scheme of 5th Sem 2018 regulation (MCA), refer to MCA 5th Sem 2018 Pattern Elective-1 (Departmental Level) Scheme. The detailed syllabus for machine learning is as follows.
MCADLE5042: Machine Learning Syllabus for MCA 5th Sem 2018 Pattern Mumbai University (Elective-1 (Departmental Level))
Prerequisites:
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Course Educational Objectives (CEO):
At the end of the course, the students will be able to
- Understand Machine Learning and its techniques.
- Study regression, classification with AdaBoost and clustering methods.
- Understand support vector machine, Dimensionality reduction, Anomaly Detection, Recommender Systems
Course Outcomes:
At the end of the course, the students will be able to
- Analyze the Machine Learning techniques.
- Apply regression, classification with AdaBoost and clustering methods to real world applications.
- Describe support vector machine, Dimensionality reduction, Anomaly Detection, Recommender Systems
1. Understand Machine Learning
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2. Supervised LearningClassification
Introduction to Supervised Learning: Classification, Decision Tree Representation- Appropriate problem for Decision Learning, Decision Tree Algorithm, Hyperspace Search in Decision Tree Naive Bayes- Bayes Theorem , Classifying with Bayes Decision Theory , Conditional Probability, Bayesian Belief Network 08
3. Supervised LearningRegression
Regression: Linear Regression- Predicting numerical value, Finding best fit line with linear regression, Regression Tree- Using CART for regression Logistic Regression – Classification with Logistic Regression and the Sigmoid Function 08
4. Support Vector Machine
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5. Improving classification with the AdaBoost
Classifier using multiple samples of the data set, Improving classifier by focusing on error, weak learner with a decision stump, Implementing the AdaBoost algorithm, Classifying with AdaBoost 08
6. Unsupervised Learning
Clustering: Learning from unclassified data -Introduction to clustering, K- Mean Clustering, Expectation-Maximization Algorithm(EM algorithm),Hierarchical Clustering, Supervised Learning after clustering 08
7. Additional Core Techniques
For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
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Reference Books:
- Machine Learning in Action By Peter Harrington By Manning
- Machine Learning, T. Mitchell, McGraw-Hill, 1997.
- Introduction to Machine LearningBy EthemAlpaydin,MIT Press
- Understanding Machine Learning From Theory to Algorithms By ShaiShalev-Shwartz and Shai Ben David, Cambridge University Press
- Data Mining Concepts and Techniques, J. Han and Kamber
Web References:
- http://www.infoworld.com/article/2853707/robotics/11-open-source-tools-machine-learning.html#slide12
- http://www.ibm.com/developerworks/library/os-recommender1/
Assessment:
Internal: Assessment consists of two tests (T1 and T2) .The final marks should be the average of the two tests. End Semester Theory Examination: Guidelines for setting up the question paper.
- Question paper will comprise of total six questions.
- Question Number One should be compulsory.
- All question carry equal marks.
- Students can attempt any three from the remaining.
- Questions will be mixed in nature (for example supposed Q.2 has part a from module 3 then part b will be from any module other than module 3).
In question paper weightage of each module will be proportional to number of respective lecture hours as mention in the syllabus.
For detail Syllabus of all subjects of MCA 5th Sem, 2018 regulation, visit MCA 5th Sem Subjects of 2018 Pattern.