ADVANCED MACHINE LEARNING AND KNOWLEDGE DISCOVERYModulo ADVANCED MACHINE LEARNING
Academic Year 2022/2023 - Docente: Vincenza CARCHIOLORisultati di apprendimento attesi
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.
Course Structure
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.
Required Prerequisites
Python programming language, Linear Algebra
Attendance of Lessons
Strongly recommended. Attending and actively participating in the classroom activities will contribute positively towards the overall assessment of the oral exam.
Detailed Course Content
Introduction to the Course
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Introduction to Machine Learning
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Review of data Characteristics of Data and Preparation and Preprocessing
Supervised Learning
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Classification and Prediction using K-Nearest-Neighbor
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Classifying with Probability Theory; Naïve Bayes
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Building Decision Trees
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Regression models
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Evaluating predictive models
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Ensemble Models: Bagging and Boosting
Unsupervised Learning
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Clustering using K-Means
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Hierarchical Clustering
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Association Rule discovery
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Principal Component Analysis and Dimensionality Reduction
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Singular Value Decomposition
Brief note on Advance Topics
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Matrix Factorization
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Support Vector Machines
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Search and Optimization Techniques
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Markov models; time series analysis, sequential pattern mining
Real application domains
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Text Mining and document analysis/filtering
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Content analysis, TFxIDF transformation, text categorization, document clustering
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Recommender systems
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Neighborhood methods (user- and item-based)
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Matrix factorization
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Marketing and finance data analysis
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- Laboraroty activity
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Brief Python review
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The package Numpy, Pandas , matplotlib and seaborn
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Scikit-learn: a machine learning library for Python
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Classification, Regression, Clustering, Dimensionality Reduction, Model Selection, Preprocessing
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Textbook Information
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Introduction to Machine Learning, Fourth Edition, By Ethem Alpaydin, MitPress ISBN: 9780262043793. 2020
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Python Data Science Essentials - Third Edition by Alberto Boschetti, Luca Massaron, Packt Publishing, ISBN: 9781789537864, 2020
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Teaching materials and reading paper list provided by the instructor
Course Planning
Subjects | Text References | |
---|---|---|
1 | Introduction to Machine Learning | 1,3 |
2 | Python review | 3 |
3 | Pandas, Numpy, Matplotlib | 2,3 |
4 | Classification and Prediction: K-Nearest-Neighbor | 1 |
5 | Classification and Naive Bayes | 1 |
6 | Decision Tree | 1,3 |
7 | Regression models | 1 |
8 | Evaluating predictive models | 1 |
9 | Ensemble Models: Bagging and Boosting | 1 |
10 | Clustering using K-Means | 1 |
11 | Hierarchical Clustering | 1 |
12 | Association Rule discovery | 1 |
13 | Dimensional reduction | 1 |
14 | Singular Value Decomposition | 1 |
15 | Advanced topic | 1,3 |
16 | Sckit learn | 3 |
17 | ML in NLP | 3 |
18 | ML for Reccomender System | 3 |
Learning Assessment
Learning Assessment Procedures
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:
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40%: Final assignments
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20% Intermediate assignments
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40%: Oral discussion
Learning assessment may also be carried out on line, should the conditions require it.
Examples of frequently asked questions and / or exercises
Examples of questions and exercises are available on the Studium platform and on the course website.