ADVANCED MACHINE LEARNING AND KNOWLEDGE DISCOVERYModule ADVANCED MACHINE LEARNING
Academic Year 2022/2023 - Teacher: Vincenza CARCHIOLOExpected Learning Outcomes
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
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
- 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 |