This page is dedicated for Savitribai Phule Pune University’s Fourth Year of Information Technology Engineering (2015 Course) subject “**Machine Learning and Applications**”

- Prerequisites:

– 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.

**The Textbooks:**

- 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
- Classification
- 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)