IT354 MACHINE LEARNING
Course Coordinator: - Prof. Hemant Yadav
| Title of the unit | Minimum number of hours | |
|---|---|---|
| 1 |
Introduction to Machine Learning |
08 |
| 2 | Supervised Learning | 16 |
| 3 |
Neural Networks and Deep Learning |
12 |
| 4 | Unsupervised Learning | 10 |
| 5 | Model Evaluations | 06 |
| 6 | Applications and Case Study | 08 |
| 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 |
08 |
| 2 |
Supervised Learning
Linear Regression, Logistic Regression, K Nearest Neighbours, Overfitting and Regularization, Support Vector Machines. |
16 |
| 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, Need for Deep representations, Building blocks of Deep Neural Networks, CNN |
12 |
| 4 |
Model Evaluations
Training Testing sets, Learning Curves, Confusion Matrix, Gain and Lift Chart, Root Mean Squared Error, Cross Validation, ROC curves |
10 |
| 5 |
Unsupervised Learning
K-Means Clustering, Hierarchical Clustering, Association Rule Learning, Dimensionality Reduction (PCA, SVD) |
06 |
| 6 |
Applications and Case Study
Machine Learning Applications Across Industries (Healthcare, Retail, Financial Services, Manufacturing, Hospitality) ML offerings AI Startups (Tips, Tricks, Definitions) |
08 |
| 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 |