9793877 - ADVANCED MACHINE LEARNING AND KNOWLEDGE DISCOVERY
Modulo 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 

Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.

Python programming language, Linear Algebra

Strongly recommended. Attending and actively participating in the classroom activities will contribute positively towards the overall assessment of the oral exam.

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

  • Brief Python review

  • 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

There will be one assignment and one final exam. The assignments will contain written questions that require some Python programming. The final exam consists  a final assignment and an oral discussion concerning all course material.  

The final assignment concerns comparative analysis on a given problem that must be presented in a final report and discussed in an oral discussion. The vote on the advanced machine learning module will account for 40% of the total grade for the entire course.

The grading policy for the AML module is:

• 40%: Final assignments

• 20% Intermediate assignments

• 40%: Oral discussion

Learning assessment may also be carried out on line, should the conditions require it.

Examples of questions and exercises are available on the Studium platform and on the course website.