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A comprehensive academic resource for Machine Learning (ML), covering supervised and unsupervised learning, neural networks, optimization techniques, and classification/clustering algorithms.
Overview · Contents · Reference Books · Personal Preparation · Assignments · Quizzes · Mock Test · Internal Assessment Test · Semester Exam · Question Papers · Submission Report · Syllabus · Usage Guidelines · License · About · Acknowledgments
Machine Learning (CSDLO6021) is a Department Level Optional Course (DLOC) in the Third Year (Semester VI) of the Computer Engineering curriculum at the University of Mumbai. This course provides a foundational understanding of machine learning models, algorithms, and their applications in solving real-world problems.
The curriculum encompasses several key domains in Machine Learning:
- Introduction to Machine Learning: Basic concepts, types of learning (Supervised, Unsupervised, Reinforcement).
- Neural Networks: Perceptrons, activation functions, backpropagation.
- Optimization Techniques: Gradient descent, loss functions, hyperparameter tuning.
- Regression and Trees: Linear regression, logistic regression, decision trees.
- Classification and Clustering: K-Nearest Neighbors (KNN), Support Vector Machines (SVM), K-Means clustering.
- Dimensionality Reduction: Principal Component Analysis (PCA).
This repository represents a curated collection of study materials, reference books, assignments, and personal preparation notes compiled during my academic journey. The primary motivation for creating and maintaining this archive is simple yet profound: to preserve knowledge for continuous learning and future reference.
As a computer engineer, understanding machine learning paradigms is crucial for developing intelligent systems and analyzing data-driven problems. This repository serves as my intellectual reference point: a resource I can return to for relearning concepts, reviewing methodologies, and strengthening understanding when needed.
Why this repository exists:
- Knowledge Preservation: To maintain organized access to comprehensive study materials beyond the classroom.
- Continuous Learning: To support lifelong learning by enabling easy revisitation of fundamental ML concepts.
- Academic Documentation: To authentically document my learning journey through Machine Learning.
- Community Contribution: To share these resources with students and learners who may benefit from them.
Note
All materials in this repository were created, compiled, and organized by me throughout my undergraduate program (2018-2022) as part of my coursework, laboratory assignments, and project implementations.
This collection includes comprehensive reference materials covering major topics:
| # | Resource | Description |
|---|---|---|
| 1 | ML Techmax | Standard Textbook (Techmax) |
| 2 | ML Easy Solutions | Solved questions and exam-oriented summaries |
| 3 | ML Toppers Solutions | High-scoring answer references |
| 4 | ML - Toppers Solutions (2019) | Previous year toppers solutions |
| 5 | ML Question Bank | Practice questions and important topics |
| 6 | ML - IMCQ | Important MCQs for exams |
| 7 | BH Plan | Study planning and strategy |
| 8 | Frequency Table | Topic frequency analysis for exams |
Study materials and planning resources for effective exam preparation:
| # | Resource | Description |
|---|---|---|
| 1 | Blueprint | ML exam blueprint and marking scheme |
| 2 | Semester 6 Timetable | Academic schedule for Semester VI |
| 3 | Computer Semester 6 Timetable | Detailed computer engineering timetable |
| 4 | Subject Choice | Department Level Optional Subject Choice |
Academic assignments for comprehensive learning and practice:
| # | Assignment | Description | Date | Marks |
|---|---|---|---|---|
| 1 | Assignment 1 | Issues & Applications of ML, Types of Learning, ANN Architecture, McCulloch-Pitts Model | April 28, 2021 | 9/10 |
| 2 | Assignment 2 | Markov Chain Models, Expectation-Maximization (EM) Algorithm, Radial Basis Function Network (RBFN) | May 03, 2021 | 9/10 |
| 3 | Assignment 3 | Dimensionality Reduction (PCA), Optimization (Hill Simplex, Steepest Descent), SVD, Regression & Decision Trees | May 03, 2021 | 9/10 |
Topics Covered: Issues in Machine Learning · Applications of ML · Types of Learning · ANN Architecture · McCulloch-Pitts Model · Markov Chain Models · Expectation-Maximization (EM) Algorithm · Radial Basis Function Network (RBFN) · Dimensionality Reduction (PCA) · Singular Value Decomposition (SVD) · Optimization Techniques (Hill Simplex, Steepest Descent) · Least Squares Regression · Decision Trees (Gini Index)
ML-specific quizzes conducted during the course:
| # | Quiz | Topics | Marks |
|---|---|---|---|
| 1 | Quiz 1 | Introduction on Machine Learning | 10/10 |
| 2 | Quiz 2 | Neural Network | 10/10 |
| 3 | Quiz 3 | Optimization Techniques | 10/10 |
| 4 | Quiz 4 | Regression & Tree | 10/10 |
| 5 | Quiz 5 | Classification & Clustering | 10/10 |
| 6 | Quiz 6 | PCA | 10/10 |
Technical mock test conducted for placement preparation:
| # | Resource | Description |
|---|---|---|
| 1 | Technical Mock Test | Campus Corners Mock Test for Terna Engineering College |
Internal assessment evaluations conducted during the course:
| # | Resource | Description | Marks |
|---|---|---|---|
| 1 | Question Paper | ML Internal Assessment Test 1 Question Paper | — |
| 2 | Answer Sheet | ML Internal Assessment Test 1 Answer Sheet | 17/20 |
| 3 | MCQ | ML Internal Assessment Test 1 MCQ | — |
| # | Resource | Description | Marks |
|---|---|---|---|
| 1 | Answer Sheet | ML Internal Assessment Test 2 Answer Sheet | — |
Additional Resources:
| # | Resource | Description |
|---|---|---|
| 1 | Answer Sheet Template | IAT Answer Sheet Template |
Important
COVID-19 Impact: This coursework was completed during the COVID-19 pandemic. All examinations and assessments were conducted in a digital format.
Final semester examination submission:
| # | Resource | Description | Date |
|---|---|---|---|
| 1 | MCQ | ML Semester Exam MCQ Paper | June 11, 2021 |
| 2 | Question 2 | ML Semester Exam Answer Sheet (Q2) | June 11, 2021 |
| 3 | Question 3 | ML Semester Exam Answer Sheet (Q3) | June 11, 2021 |
Additional Resources:
| # | Resource | Description |
|---|---|---|
| 1 | Answer Sheet Template | Semester Exam Answer Sheet Template |
| 2 | Questions Document | ML Exam Questions |
| 3 | Reference Document | ML Exam Reference |
| 4 | ML Answersheet Template | Answer Sheet Layout |
| 5 | Seat No | Seat No Word Document |
University of Mumbai examination papers from 2012-2019:
| # | Exam Session | Syllabus | Resource |
|---|---|---|---|
| 1 | May 2019 | CBCGS | View |
| 2 | December 2019 | CBCGS | View |
| 3 | May 2018 | CBCGS | View |
| 4 | December 2018 | CBCGS | View |
| 5 | May 2017 | CBCGS | View |
| 6 | December 2017 | CBCGS | View |
| 7 | May 2016 | CBCGS | View |
| 8 | December 2016 | CBCGS | View |
| 9 | May 2015 | CBGS | View |
| 10 | December 2015 | CBGS | View |
| 11 | May 2014 | CBGS | View |
| 12 | December 2014 | CBGS | View |
| 13 | May 2013 | CBGS | View |
| 14 | December 2013 | CBGS | View |
| 15 | May 2012 | CBGS | View |
| 16 | December 2012 | CBGS | View |
Course completion documentation:
| # | Document | Description |
|---|---|---|
| 1 | Submission Report | Final coursework submission report |
Official CBCGS Syllabus
Complete Third Year Computer Engineering syllabus document from the University of Mumbai, including detailed course outcomes, assessment criteria, and module specifications for Machine Learning.
Important
Always verify the latest syllabus details with the official University of Mumbai website, as curriculum updates may occur after this repository's archival date.
This repository is openly shared to support learning and knowledge exchange across the academic community.
For Students
Use these resources as reference materials for understanding machine learning concepts, algorithms, and preparing for examinations. All content is organized for self-paced learning.
For Educators
These materials may serve as curriculum references or supplementary teaching resources. Attribution is appreciated when utilizing content.
For Researchers
The documentation and organization may provide insights into academic resource curation and educational content structuring.
This repository and all linked academic content are made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0). See the LICENSE file for complete terms.
Note
Summary: You are free to share and adapt this content for any purpose, even commercially, as long as you provide appropriate attribution to the original author.
Created & Maintained by: Amey Thakur
Academic Journey: Bachelor of Engineering in Computer Engineering (2018-2022)
Institution: Terna Engineering College, Navi Mumbai
University: University of Mumbai
This repository represents a comprehensive collection of study materials, reference books, assignments, and personal preparation notes curated during my academic journey. All content has been carefully organized and documented to serve as a valuable resource for students pursuing Machine Learning.
Connect: GitHub · LinkedIn · ORCID
Grateful acknowledgment to the faculty members of the Department of Computer Engineering at Terna Engineering College for their guidance and instruction in Machine Learning. Their clear teaching and continued support helped develop a strong understanding of algorithmic foundations and model implementation.
Special thanks to the mentors and peers whose encouragement, discussions, and support contributed meaningfully to this learning experience.
Overview · Contents · Reference Books · Personal Preparation · Assignments · Quizzes · Mock Test · Internal Assessment Test · Semester Exam · Question Papers · Submission Report · Syllabus · Usage Guidelines · License · About · Acknowledgments
Computer Engineering (B.E.) - University of Mumbai
Semester-wise curriculum, laboratories, projects, and academic notes.