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Machine Learning Semester 8 (BE Fourth Year) BE Computer Engineering University of Mumbai Topics and Syllabus

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University of Mumbai Syllabus For Semester 8 (BE Fourth Year) Machine Learning: Knowing the Syllabus is very important for the students of Semester 8 (BE Fourth Year). Shaalaa has also provided a list of topics that every student needs to understand.

The University of Mumbai Semester 8 (BE Fourth Year) Machine Learning syllabus for the academic year 2021-2022 is based on the Board's guidelines. Students should read the Semester 8 (BE Fourth Year) Machine Learning Syllabus to learn about the subject's subjects and subtopics.

Students will discover the unit names, chapters under each unit, and subtopics under each chapter in the University of Mumbai Semester 8 (BE Fourth Year) Machine Learning Syllabus pdf 2021-2022. They will also receive a complete practical syllabus for Semester 8 (BE Fourth Year) Machine Learning in addition to this.

CBCGS [2019 - current]
CBGS [2015 - 2018]
Old [2000 - 2014]

University of Mumbai Semester 8 (BE Fourth Year) Machine Learning Revised Syllabus

University of Mumbai Semester 8 (BE Fourth Year) Machine Learning and their Unit wise marks distribution

University of Mumbai Semester 8 (BE Fourth Year) Machine Learning Course Structure 2021-2022 With Marking Scheme

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Syllabus

100 Introduction to Machine Learning
  • What is Machine Learning?, Key Terminology, Types of Machine Learning, Issues in Machine Learning, Application of Machine Learning, How to choose the right algorithm, Steps in developing a Machine Learning Application.
200 Learning with Regression
  • Linear Regression, Logistic Regression.
300 Learning with Trees
  • Using Decision Trees, Constructing Decision Trees, Classification and Regression Trees (CART).
400 Support Vector Machines (SVM)
  • Maximum Margin Linear Separators, Quadratic Programming solution to finding maximum margin separators, Kernels for learning non-linear functions.
500 Learning with Classification
  • Rule based classification, classification by backpropoagation, Bayesian Belief networks, Hidden Markov Models.
600 Dimensionality Reduction
  • Dimensionality Reduction Techniques, Principal Component Analysis, Independent Component Analysis.
700 Learning with Clustering
  • K-means clustering, Hierarchical clustering, Expectation Maximization Algorithm, Supervised learning after clustering, Radial Basis functions.
800 Reinforcement Learning
  • Introduction, Elements of Reinforcement Learning, Model based learning, Temporal Difference Learning, Generalization, Partially Observable States.
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