9793877 - ADVANCED MACHINE LEARNING AND KNOWLEDGE DISCOVERY
Module 9793877 - ADVANCED MACHINE LEARNING

The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries.

Lectures, hands-on exercises, paper reading, student presentations and seminars

Introduction to the Course

• Introduction to Machine Learning

•  Review of data Characteristics of Data and Preparation and Preprocessing

Supervised Learning

• Classification and Prediction using K-Nearest-Neighbor

• Classifying with Probability Theory; Naïve Bayes

• Building Decision Trees

• Regression models

• Evaluating predictive models

• Ensemble Models: Bagging and Boosting

Unsupervised Learning

• Clustering using K-Means

• Hierarchical Clustering

• Association Rule discovery 

• Principal Component Analysis and Dimensionality Reduction

• Singular Value Decomposition

Brief note on Advance Topics 

• Matrix Factorization

• Support Vector Machines

• Search and Optimization Techniques

• Markov models; time series analysis, sequential pattern mining

Real application domains

• Text Mining and document analysis/filtering

  • Content analysis, TFxIDF transformation, text categorization, document clustering

• Recommender systems

  • Neighborhood methods (user- and item-based)

  • Matrix factorization

  • Marketing and finance data analysis

• Laboraroty activity
  • The package Numpy, Pandas , matplotlib and seaborn

  • Scikit-learn: a machine learning library for Python

    • Classification, Regression, Clustering, Dimensionality Reduction, Model Selection, Preprocessing

1. Introduction to Machine Learning, Fourth Edition, By Ethem Alpaydin, MitPress ISBN: 9780262043793. 2020

2.  Python Data Science Essentials - Third Edition by Alberto Boschetti, Luca Massaron, Packt Publishing, ISBN: 9781789537864, 2020

3. Teaching materials and reading paper list provided by the instructor