IT354 MACHINE LEARNING

Title of the unit Minimum number of hours
1
Introduction to Machine Learning
10
2 Supervised Learning 12
3
Neural Networks and Deep Learning 		10
4 Unsupervised Learning 08
5 Reinforcement Learning and applications 05


Unit Number Topics Teaching Hours
1 Introduction to Machine Learning
Need for Machine Learning, Basic principles, Applications, Challenges, Types of Machine Leaning: Supervised Learning, Unsupervised Learning, Reinforcement Learning, Exploratory Data Analysis, Linear Regression, Logistic Regression
10
2 Supervised Learning
K - Nearest Neighbors, Tree based models(Decision Tree, Random Forest, XGBoost), Support Vector Machines(SVM), Regression evaluation measures (SSE, RMSE, R2 Score), Classification Evaluation measures (Accuracy, Precision, Recall , confusion Metrics, F1-Score), Over fitting and under fitting
12
3 Neural Networks and Deep Learning
Perceptron Learning, Network Overview, Neural Network Representation, Need for Non-Linear Activation Functions, Cost Function, Back propagation, Training & Validation, Deep Learning introduction and requirement, Hyper parameter tuning, Convolution Neural Nets, Recurrent Neural Nets
10
4 Unsupervised Learning
K-Means Clustering, Hierarchical Clustering, Anomaly Detection, Association Rule Learning, Dimensionality Reduction (PCA, SVD)
08
5 Reinforcement Learning and applications
Reinforcement Learning fundamentals, Q-Learning, Applications of Reinforcement Learning, Machine Learning Applications Across Industries (Healthcare, Retail, Financial Services, Manufacturing, Hospitality) ML offerings AI Startups (Tips, Tricks, Definitions), Introduction to Recommendation Systems
05


Textbooks
Machine Learning, Tom Mitchell, McGraw Hill, 1997. ISBN 0070428077Click Here
Online Course
[Learn any time, anywhere] is a support by DataCamp via online courses for this course. Datacamp provided Short videos on concepts and hands-on exercises on courses. Visit Datacamp