Course Outcome ( CO) Bloom’s Knowledge Level
At the end of course , the student will be able:
CO 1 To understand the need for machine learning for various problem solving K1 , K2
CO 2 To understand a wide variety of learning algorithms and how to evaluate
models generated from data
K1 , K3
CO 3 To understand the latest trends in machine learning K2 , K3
CO 4 To design appropriate machine learning algorithms and apply the algorithms to
a real-world problems
K4 , K6
CO 5 To optimize the models learned and report on the expected accuracy that can
be achieved by applying the models
K4, K5
DETAILED SYLLABUS 3-0-0
Unit Topic ProposedLecture
I INTRODUCTION – Learning, Types of Learning, Well defined learning
problems, Designing a Learning System, History of ML, Introduction of Machine
Learning Approaches – (Artificial Neural Network, Clustering, Reinforcement
Learning, Decision Tree Learning, Bayesian networks, Support Vector Machine,
Genetic Algorithm), Issues in Machine Learning and Data Science Vs Machine
Learning;
Ii REGRESSION: Linear Regression and Logistic Regression
BAYESIAN LEARNING - Bayes theorem, Concept learning, Bayes Optimal
Classifier, Naïve Bayes classifier, Bayesian belief networks, EM algorithm.
SUPPORT VECTOR MACHINE: Introduction, Types of support vector kernel
– (Linear kernel, polynomial kernel,and Gaussiankernel), Hyperplane – (Decision
surface), Properties of SVM, and Issues in SVM.
IIIDECISION TREE LEARNING - Decision tree learning algorithm, Inductive
bias, Inductive inference with decision trees, Entropy and information theory,
Information gain, ID-3 Algorithm, Issues in Decision tree learning.
INSTANCE-BASED LEARNING – k-Nearest Neighbour Learning, Locally
Weighted Regression, Radial basis function networks, Case-based learning.
IV ARTIFICIAL NEURAL NETWORKS – Perceptron’s, Multilayer perceptron,
Gradient descent and the Delta rule, Multilayer networks, Derivation of
Backpropagation Algorithm, Generalization, Unsupervised Learning – SOM
Algorithm and its variant;
DEEP LEARNING - Introduction,concept of convolutional neural network ,
Types of layers – (Convolutional Layers , Activation function , pooling , fully
connected) , Concept of Convolution (1D and 2D) layers, Training of network,
Case study of CNN for eg on Diabetic Retinopathy, Building a smart speaker,
Self-deriving car etc.
V REINFORCEMENT LEARNING–Introduction to Reinforcement Learning ,
Learning Task,Example of Reinforcement Learning in Practice, Learning Models
for Reinforcement – (Markov Decision process , Q Learning - Q Learning
function, Q Learning Algorithm ), Application of Reinforcement
Learning,Introduction to Deep Q Learning.
MASTER OF COMPUTER APPLICATION (MCA)
Curriculum & Evaluation Scheme MCA(III & IV semester) Page 51
GENETIC ALGORITHMS: Introduction, Components, GA cycle of
reproduction, Crossover, Mutation, Genetic Programming, Models of Evolution
and Learning, Applications.
Text books:
1. Tom M. Mitchell, ―Machine Learning, McGraw-Hill Education (India) Private Limited, 2013.
2. Ethem Alpaydin, ―Introduction to Machine Learning (Adaptive Computation and Machine Learning),
MIT Press 2004.
3. Stephen Marsland, ―Machine Learning: An Algorithmic Perspective, CRC Press, 2009.
4. Bishop, C., Pattern Recognition and Machine Learning. Berlin: Springer-Verlag.
5. M. Gopal, “Applied Machine Learning”, McGraw Hill Education
