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 2022-2023 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 2022-2023. They will also receive a complete practical syllabus for Semester 8 (BE Fourth Year) Machine Learning in addition to this.
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 2022-2023 With Marking Scheme
| # | Unit/Topic | Weightage |
|---|---|---|
| 100 | Introduction to Machine Learning | |
| 200 | Learning with Regression | |
| 300 | Learning with Trees | |
| 400 | Support Vector Machines (SVM) | |
| 500 | Learning with Classification | |
| 600 | Dimensionality Reduction | |
| 700 | Learning with Clustering | |
| 800 | Reinforcement Learning | |
| Total | - |
Syllabus
- 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.
- Linear Regression, Logistic Regression.
- Using Decision Trees, Constructing Decision Trees, Classification and Regression Trees (CART).
- Maximum Margin Linear Separators, Quadratic Programming solution to finding maximum margin separators, Kernels for learning non-linear functions.
- Rule based classification, classification by backpropoagation, Bayesian Belief networks, Hidden Markov Models.
- Dimensionality Reduction Techniques, Principal Component Analysis, Independent Component Analysis.
- K-means clustering, Hierarchical clustering, Expectation Maximization Algorithm, Supervised learning after clustering, Radial Basis functions.
- Introduction, Elements of Reinforcement Learning, Model based learning, Temporal Difference Learning, Generalization, Partially Observable States.
