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Machine Learning ( For B.Tech VIII sem)
Machine learning is an active and growing field that would require many courses to cover
completely. This course aims at the middle of the theoretical versus practical spectrum. We will
learn the concepts behind several machine learning algorithms without going deeply into the
mathematics and gain practical experience applying them. We will consider pattern recognition
and artificial intelligence perspectives, making the course valuable to students interested in
data science, engineering, and intelligent agent applications.
Module Subtitle of the Module
Topics in the module
No.
1
Introduction
Definition of learning systems.
Goals and applications of machine learning.
Aspects of developing a learning system
2
Supervised LearningParametric and Non Parametric MethodsParametric and Non
 Naïve Bayes
Parametric Methods
 Maximum Entropy
 CRF
 Hidden Markov Model
 KNN
 Vector space model and cosine similarity.
No. of
Lectures
3
7
3
Decision Trees
Representing Concepts and Recursive induction :
ID3,C4.5,CHAID
Splitting attributes:Entropy and Information Gain.
Searching for simple trees and computational
complexity.
Overfitting, noisy data, and pruning
5
4
5
Ensemble Learning
Unsupervised Learning:
Clustering
Active Learning- Bagging and Boosting
Learning from unclassified data using Implementation
and Case studies Hierarchical Aglomerative Clustering.
k-means partitional clustering. Expectation
maximization (EM)
Semi-supervised learning with EM using labeled and
unlabled data.
2
7
6
Neural Networks and
Introduction to Fuzzy
Logic
Perceptrons: representational limitation and gradient
descent training.
Multilayer networks and backpropagation.
Recurrent networks.
Fuzzy Theory and Applications
7
7
Features and
Dimensionality Reduction
Kernel Machines
Feature Extraction, PCA , LDA, Feature Scaling
4
SVM- Linear and Non- Linear Kernel functions
3
Experimental Evaluation
of Learning Algorithms
Comparing learning algorithms: cross-validation,
learning curves, and statistical hypothesis testing.
8
9
*Each module will include some open problems
*Each module will include some open problems
Total number of Lectures
Text Book:
Ethem Alpaydin, Introduction to Machine Learning, Second Edition
4
42
Recommended:
1. Stephen Marsland, Machine Learning: An Algorithmic Perspective
2. Christopher M. Bishop, Pattern Recognition and Machine Learning
3. Tom Mitchell, Machine Learning
4. Yen and Lengari, Handbook of Research on Fuzzy Information Processing in Databases
Resources:
Journals :
1. Journal of Machine Learning Research www.jmlr.org
2. Machine Learning
3. Neural Computation
4. Neural Networks
5. IEEE Transactions on Neural Networks
6. IEEE Transactions on Pattern Analysis
Conferences:
1. International Conference on Machine Learning (ICML))
2. European Conference on Machine Learning (ECML)
3. Neural Information Processing Systems(NIPS)
Varsha Garg