Third Year B.Tech (DJ 19) - Semester V - Machine Learning (DJ19ADC501)

Oct 13, 2026

Scheme and detailed syllabus for Third Year B.Tech (DJ 19) in Artificial Intelligence and Data Science, Semester V, covering the course Machine Learning and its laboratory (DJ19ADC501, DJ19ADL501). The program outlines prerequisites from data structures and object-oriented Python, learning objectives for supervised/unsupervised methods, and expected outcomes such as classification, regression, clustering, dataset evaluation, and application design. Unit-wise content spans machine learning fundamentals through Bayesian decision trees, multivariate methods, dimensionality reduction, clustering, and artificial neural learning, followed by books, experiments, and an evaluation scheme with theory and laboratory components.

LanguageEnglish

CategoryPaper Templates

FormatPDF

Pages43

Reading Time15 min

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Machine Learning - Teaching and Evaluation Scheme

Prerequisites and Course Objectives

Outcomes and Unit-wise Detailed Syllabus

Recommended Books and Suggested Experiments

Evaluation Scheme (Semester End and Continuous Assessment)

Shri Vile Parle Kelavani Mandal’s Dwarkadas J. Sanghvi College of Engineering (Autonomous College Affiliated to the University of Mumbai) Scheme and detailed syllabus Third Year B.Tech (DJ 19) in Artificial Intelligence and Data Science (Semester V) | Program: Third Year B.Tech. in Artificial Intelligence & Data Science | | | | | | | | Semester : V | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Course : Machine Learning | | | | | | | | Course Code: DJ19ADC501 | | | | Course: Machine Learning Laboratory | | | | | | | | Course Code: DJ19ADL501 | | | | Teaching Scheme (Hours / week) | | | | Evaluation Scheme | | | | | | | | | | | | Semester End Examination Marks (A) | | | Continuous Assessment Marks (B) | | | Total
marks
(A+ B) | | Lectures | Practical | Tutorial | Total
Credits | Theory | | | Term
Test 1 | Term
Test 2 | Avg. | | | | | | | 75 | | | 25 | 25 | 25 | 100 | | | | | | Laboratory Examination | | | Term work | | Total
Ter
m
work | 50 | | 3 | 2 | -- | 4 | Oral | Practica
l | Oral
&
Practi
cal | Labor
atory
Work | Tutorial / Mini project / presentation/ Journal | | | | | | | | 25 | -- | -- | 15 | 10 | 25 | | Prerequisite: Knowledge of Data Structures, Object oriented Python Programming Language, Foundation of Data Science Course Objectives: 1. To get familiarize concepts of Machine Learning theoretically & practically 2. To explore the fundamentals field of Machine Learning through core concepts of supervised and unsupervised learning, Dimensionality reduction, clustering & SVM Outcomes: Students will be able to 1. Classify given problems into classification, clustering and regression problems 2. Apply machine learning techniques for a given problem 3. Examine the dataset, choose appropriate algorithm and evaluate the results. 4. Design applications using machine learning algorithms | Detailed Syllabus: (Unit wise) | | | | --- | --- | --- | | Unit | Description | Duration | | 1 | Introduction to Machine Learning:
What is Machine Learning, Examples of Machine Learning Applications: Learning Association, Classification, regression, Unsupervised Learning , reinforcement learning
Supervised Learning: Learning class from examples, Vapnik-Chervonenkis Dimension, Probably Approximately Correct Learning, Noise, Learning Multiple Classes, Regression, Model Selection and Generalization, Dimensions of a Supervised Machine Learning Algorithm | 6 | | 2 | Bayesian Decision Tree:
Introduction, Classification, Losses & Risks, Discriminant functions, Association rules
Parametric Methods: Introduction, Maximum Likelihood Estimation, Evaluating an Estimate, The Bayes Estimator, Parametric Classification, Regression, Turing model Complexity, Model selection procedure. | 8 | | 3 | Multivariate Methods: Multivariate data, Parameter estimation, Estimation of missing values, Multivariate Normal distribution, Multivariate Classification, Tuning complexity, Discrete features, Multivariate regression. | 6 | | 4 | Dimensionality Reduction: Introduction, subset selection, PCA, Feature Embedding, Factor analysis, Singular value Decomposition & Matrix Factorization, Multidimensional scaling, LDA,CCA, Isomap, LLE, Laplacian Eigen maps | 6 | | 5 | Clustering: Introduction, Mixture Densities, K-means clustering, ExpectationMaximization Algorithm, Mixture of latent variable models, Types of Clustering Introduction to Support Vector Machine: Support Vectors, Functional Margin, Geometric Margin, Optimization problem, Lagrange Duality, KKT condition, Maximum margin with noise, Non-linear SVM and Kernel Function | 8 | | 6 | Introduction to Artificial Neural Learning: History of Deep Learning, Fundamental concepts of biological Neural Networks, Important terminologies of ANN: Activation functions: weights, bias, threshold, learning rate, momentum factor; McCulloch Pitts Neuron:

FAQs

What prerequisites are required for the Machine Learning course (DJ19ADC501)?

The syllabus lists prerequisite knowledge of Data Structures and object oriented Python programming, along with a foundation of the Data Science course.

What learning outcomes are expected from students after completing the course?

Students should be able to classify problems into classification, clustering, and regression; apply machine learning techniques; examine datasets to choose and evaluate algorithms; and design applications using machine learning.

Which topics are covered across the six syllabus units?

Units cover machine learning introduction; Bayesian decision tree and parametric methods; multivariate methods; dimensionality reduction; clustering and SVM basics; and an introduction to artificial neural learning and deep learning fundamentals.

Oct 13, 2026

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