Machine Learning and Applications

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:

  1. Ethem Alpaydin: Introduction to Machine Learning
  2. Peter Flach: Machine Learning: The Art and Science of Algorithms that Make Sense of Data

The Reference books:

  1. C. M. Bishop: Pattern Recognition and Machine Learning
  2. Ian H Witten, Eibe Frank, Mark A Hall: Data Mining, Practical Machine Learning Tools and Techniques
  3. Parag Kulkarni: Reinforcement Learning and Systemic Machine Learning for Decision Making
  4. Nikhil Buduma: Fundamentals of Deep Learning
  5. Hastie, Tibshirani, Friedman: Introduction to Statistical Machine Learning with Applications in R
  6. Kevin P Murphy: Machine Learning – A Probabilistic Perspective

The Theory Presentations:

  1. Introduction To Machine Learning
  2. Dimensionality Reduction
  3. Classification
  4. Regression And Generalization
  5. Logic Based And Algebraic Models
  6. Probabilistic Models
  7. Ensemble Learning
  8. Deep Learning

Practical Assignments (Computer Laboratory – VII):

  1. Study of open source software for Machine Learning
  2. Supervised Learning – Regression (using R)
  3. Market Basket Analysis (using R)
  4. K-Means algorithm for clustering (using Python)
  5. SVM for performing classification (using Python)
  6. Creating & Visualizing Neural Network (using Python)
  7. Performance measurements (using WEKA)
  8. Principal Component Analysis (using R)