This page is dedicated for Savitribai Phule Pune University’s Fourth Year of Information Technology Engineering (2015 Course) subject “Machine Learning and Applications”
– Linear Algebra and Calculus, Probability Basics
- Course Objectives:
– Understanding Human learning aspects.
– Understanding primitives and methods in learning process by computer.
– Understanding nature of problems solved with Machine Learning
- Course Outcomes:
By the end of the course, students should be able to
– model the learning primitives.
– build the learning model.
– tackle real world problems in the domain of Data Mining and Big Data Analytics, Information Retrieval, Computer vision, Linguistics and Bioinformatics.
- Ethem Alpaydin: Introduction to Machine Learning
- Peter Flach: Machine Learning: The Art and Science of Algorithms that Make Sense of Data
The Reference books:
- C. M. Bishop: Pattern Recognition and Machine Learning
- Ian H Witten, Eibe Frank, Mark A Hall: Data Mining, Practical Machine Learning Tools and Techniques
- Parag Kulkarni: Reinforcement Learning and Systemic Machine Learning for Decision Making
- Nikhil Buduma: Fundamentals of Deep Learning
- Hastie, Tibshirani, Friedman: Introduction to Statistical Machine Learning with Applications in R
- Kevin P Murphy: Machine Learning – A Probabilistic Perspective
The Theory Presentations:
- Introduction To Machine Learning
- Dimensionality Reduction
- Regression And Generalization
- Logic Based And Algebraic Models
- Probabilistic Models
- Ensemble Learning
- Deep Learning
Practical Assignments (Computer Laboratory – VII):
- Study of open source software for Machine Learning
- Supervised Learning – Regression (using R)
- Market Basket Analysis (using R)
- K-Means algorithm for clustering (using Python)
- SVM for performing classification (using Python)
- Creating & Visualizing Neural Network (using Python)
- Performance measurements (using WEKA)
- Principal Component Analysis (using R)