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.
This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge.
Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of real-world applications ranging from computer vision, machine translation and medical image analysis.
The learning objectives are:
Knowledge and understanding
Applying knowledge and understanding
Lectures, hands-on exercises, paper reading, student presentations and seminars
The main teaching methods are as follows:
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.
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
Scikit-learn: a machine learning library for Python
Classification, Regression, Clustering, Dimensionality Reduction, Model Selection, Preprocessing
The KD module consists of two parts: the first one will be addressing the general and modern techniques based on deep learning paradigm to create KD systems from data, while the second one on how to extract, represent and visualize knowledge from data and trained models.
Part I: Methods and Architectures
Neural Networks and Backpropagation
Convolutional Neural Networks
Recurrent Neural Networks
Part II: Knowledge Discovery from Data and Models
Unsupervised Learning with Deep Networks
Reinforcement Learning
Deep Learning Frameworks:
Applications:
Introduction to Machine Learning, Fourth Edition, By Ethem Alpaydin, MitPress ISBN: 9780262043793. 2020
Python Data Science Essentials - Third Edition by Alberto Boschetti, Luca Massaron, Packt Publishing, ISBN: 9781789537864, 2020
Teaching materials and reading paper list provided by the instructor
Deep Learning. I. Goodfellow, Y. Bengio and A. Courville, MIT Press, 2016
Programming PyTorch for Deep Learning, I. Pointer, O'Reilly Media
Teaching materials and reading paper list provided by the instructor