The course aims to delve into main foundation methods and techniques for developing Machine Learning algorithms: those that are supervised, unsupervised, and by reinforcement; and to use them as tools for developing applications in specific domains. Aspects of the main areas of the discipline, including regression, classification and clustering, will be studied.
Lectures and exercises conducted during the course will allow students to learn methods and techniques for choosing and training specific machine learning approaches from real datasets on various domains, e.g., health care, financial analysis, video games, computer vision, recommender systems.
Lectures and exercises conducted during the course will allow students to learn methods and techniques for choosing and training specific machine learning approaches from real datasets on various domains, e.g., health care, financial analysis, video games, computer vision, recommender systems.
Curriculum
teacher profile teaching materials
- Areas of interest in Machine Learning.
- Potenzialità dei modelli e dei metodi di ML.
2. Regression
- Introduction to Linear Regression.
- Overfitting in Regression.
- Regularization: Ridge Regression.
- Feature Selection and Lasso.
3. Classification
- Logistic Regression for classification.
- Overfitting in Classification.
- Boosting. AdaBoost algorithm.
- Naïve Bayes.
- Support Vector Machines.
4. Clustering
- Algoritmi k-means e k-means++ algorithms
- Expectation Maximization.
- Clustering gerarchico.
5. Artificial Neural Networks
- Architecture of Artificial Neural Networks.
- Backpropagation Learning algorithm.
- Applications of Artificial Neural Networks.
6. Keras and TensorFlow environments
- Keras and TensorFlow languages for developing ML applications.
- GPU-based architectures. Nvidia Tesla and Volta GPUs.
- Using TensorFlow with GPU support.
7. Dimensionality Reduction
- Data compression and visualization.
- Principal Component Analysis (PCA).
- Choosing the number of principal components.
- Applications in Recommender Systems.
8. Reinforcement Learning
- Markov Decision Process.
- Dynamic Programming.
- Reinforcement Learning algorithms.
9. Introduction to Deep Learning
- Introduction to Deep Forward Networks.
- Notes on Convolutional Neural Networks (CNN).
- Notes on Generative Adversarial Networks (GAN).
10. Case Studies and Projects
Various case studies will be presented and projects will be proposed in which to apply the notions learned on various domains of interest. In particular, the topics covered may concern, among other things, applications of ML methods and techniques in the following areas:
• Social Media Analysis (sentiment analysis, fake news detection, fake users detection, ecc.)
• Financial Machine Learning (algorithmic trading, ecc.)
• Recommender Systems (social RecSys, cultural heritage RecSys, e-commerce RecSys, ecc.)
• Data Science (prediction functions per applicazioni pratiche, ecc.)
• Computer Vision (object detection, face detection, face recognition, content-based video analysis, ecc.)
• Bioinformatics (recognition of genetic sequences, ecc.)
Mutuazione: 20810266 Machine Learning in Ingegneria informatica LM-32 MICARELLI ALESSANDRO, GASPARETTI FABIO
Programme
1. Introduction to the Course- Areas of interest in Machine Learning.
- Potenzialità dei modelli e dei metodi di ML.
2. Regression
- Introduction to Linear Regression.
- Overfitting in Regression.
- Regularization: Ridge Regression.
- Feature Selection and Lasso.
3. Classification
- Logistic Regression for classification.
- Overfitting in Classification.
- Boosting. AdaBoost algorithm.
- Naïve Bayes.
- Support Vector Machines.
4. Clustering
- Algoritmi k-means e k-means++ algorithms
- Expectation Maximization.
- Clustering gerarchico.
5. Artificial Neural Networks
- Architecture of Artificial Neural Networks.
- Backpropagation Learning algorithm.
- Applications of Artificial Neural Networks.
6. Keras and TensorFlow environments
- Keras and TensorFlow languages for developing ML applications.
- GPU-based architectures. Nvidia Tesla and Volta GPUs.
- Using TensorFlow with GPU support.
7. Dimensionality Reduction
- Data compression and visualization.
- Principal Component Analysis (PCA).
- Choosing the number of principal components.
- Applications in Recommender Systems.
8. Reinforcement Learning
- Markov Decision Process.
- Dynamic Programming.
- Reinforcement Learning algorithms.
9. Introduction to Deep Learning
- Introduction to Deep Forward Networks.
- Notes on Convolutional Neural Networks (CNN).
- Notes on Generative Adversarial Networks (GAN).
10. Case Studies and Projects
Various case studies will be presented and projects will be proposed in which to apply the notions learned on various domains of interest. In particular, the topics covered may concern, among other things, applications of ML methods and techniques in the following areas:
• Social Media Analysis (sentiment analysis, fake news detection, fake users detection, ecc.)
• Financial Machine Learning (algorithmic trading, ecc.)
• Recommender Systems (social RecSys, cultural heritage RecSys, e-commerce RecSys, ecc.)
• Data Science (prediction functions per applicazioni pratiche, ecc.)
• Computer Vision (object detection, face detection, face recognition, content-based video analysis, ecc.)
• Bioinformatics (recognition of genetic sequences, ecc.)
Prerequisiti
Computer Science Foundations.Core Documentation
Lecture notes by the professor.Type of delivery of the course
Lessons and exercises. Project supervision.Attendance
Attendance is not compulsory, but it is strongly recommended.Type of evaluation
Written test, practical test. teacher profile teaching materials
- Areas of interest in Machine Learning.
- Potenzialità dei modelli e dei metodi di ML.
2. Regression
- Introduction to Linear Regression.
- Overfitting in Regression.
- Regularization: Ridge Regression.
- Feature Selection and Lasso.
3. Classification
- Logistic Regression for classification.
- Overfitting in Classification.
- Boosting. AdaBoost algorithm.
- Naïve Bayes.
- Support Vector Machines.
4. Clustering
- Algoritmi k-means e k-means++ algorithms
- Expectation Maximization.
- Clustering gerarchico.
5. Artificial Neural Networks
- Architecture of Artificial Neural Networks.
- Backpropagation Learning algorithm.
- Applications of Artificial Neural Networks.
6. Keras and TensorFlow environments
- Keras and TensorFlow languages for developing ML applications.
- GPU-based architectures. Nvidia Tesla and Volta GPUs.
- Using TensorFlow with GPU support.
7. Dimensionality Reduction
- Data compression and visualization.
- Principal Component Analysis (PCA).
- Choosing the number of principal components.
- Applications in Recommender Systems.
8. Reinforcement Learning
- Markov Decision Process.
- Dynamic Programming.
- Reinforcement Learning algorithms.
9. Introduction to Deep Learning
- Introduction to Deep Forward Networks.
- Notes on Convolutional Neural Networks (CNN).
- Notes on Generative Adversarial Networks (GAN).
10. Case Studies and Projects
Various case studies will be presented and projects will be proposed in which to apply the notions learned on various domains of interest. In particular, the topics covered may concern, among other things, applications of ML methods and techniques in the following areas:
• Social Media Analysis (sentiment analysis, fake news detection, fake users detection, ecc.)
• Financial Machine Learning (algorithmic trading, ecc.)
• Recommender Systems (social RecSys, cultural heritage RecSys, e-commerce RecSys, ecc.)
• Data Science (prediction functions per applicazioni pratiche, ecc.)
• Computer Vision (object detection, face detection, face recognition, content-based video analysis, ecc.)
• Bioinformatics (recognition of genetic sequences, ecc.)
Mutuazione: 20810266 Machine Learning in Ingegneria informatica LM-32 MICARELLI ALESSANDRO, GASPARETTI FABIO
Programme
1. Introduction to the Course- Areas of interest in Machine Learning.
- Potenzialità dei modelli e dei metodi di ML.
2. Regression
- Introduction to Linear Regression.
- Overfitting in Regression.
- Regularization: Ridge Regression.
- Feature Selection and Lasso.
3. Classification
- Logistic Regression for classification.
- Overfitting in Classification.
- Boosting. AdaBoost algorithm.
- Naïve Bayes.
- Support Vector Machines.
4. Clustering
- Algoritmi k-means e k-means++ algorithms
- Expectation Maximization.
- Clustering gerarchico.
5. Artificial Neural Networks
- Architecture of Artificial Neural Networks.
- Backpropagation Learning algorithm.
- Applications of Artificial Neural Networks.
6. Keras and TensorFlow environments
- Keras and TensorFlow languages for developing ML applications.
- GPU-based architectures. Nvidia Tesla and Volta GPUs.
- Using TensorFlow with GPU support.
7. Dimensionality Reduction
- Data compression and visualization.
- Principal Component Analysis (PCA).
- Choosing the number of principal components.
- Applications in Recommender Systems.
8. Reinforcement Learning
- Markov Decision Process.
- Dynamic Programming.
- Reinforcement Learning algorithms.
9. Introduction to Deep Learning
- Introduction to Deep Forward Networks.
- Notes on Convolutional Neural Networks (CNN).
- Notes on Generative Adversarial Networks (GAN).
10. Case Studies and Projects
Various case studies will be presented and projects will be proposed in which to apply the notions learned on various domains of interest. In particular, the topics covered may concern, among other things, applications of ML methods and techniques in the following areas:
• Social Media Analysis (sentiment analysis, fake news detection, fake users detection, ecc.)
• Financial Machine Learning (algorithmic trading, ecc.)
• Recommender Systems (social RecSys, cultural heritage RecSys, e-commerce RecSys, ecc.)
• Data Science (prediction functions per applicazioni pratiche, ecc.)
• Computer Vision (object detection, face detection, face recognition, content-based video analysis, ecc.)
• Bioinformatics (recognition of genetic sequences, ecc.)
Prerequisiti
Computer Science Foundations.Core Documentation
Lecture notes by the professor.Attendance
Attendance is not compulsory, but it is strongly recommended.Type of evaluation
Written test, practical test. teacher profile teaching materials
- Areas of interest in Machine Learning.
- Potenzialità dei modelli e dei metodi di ML.
2. Regression
- Introduction to Linear Regression.
- Overfitting in Regression.
- Regularization: Ridge Regression.
- Feature Selection and Lasso.
3. Classification
- Logistic Regression for classification.
- Overfitting in Classification.
- Boosting. AdaBoost algorithm.
- Naïve Bayes.
- Support Vector Machines.
4. Clustering
- Algoritmi k-means e k-means++ algorithms
- Expectation Maximization.
- Clustering gerarchico.
5. Artificial Neural Networks
- Architecture of Artificial Neural Networks.
- Backpropagation Learning algorithm.
- Applications of Artificial Neural Networks.
6. Keras and TensorFlow environments
- Keras and TensorFlow languages for developing ML applications.
- GPU-based architectures. Nvidia Tesla and Volta GPUs.
- Using TensorFlow with GPU support.
7. Dimensionality Reduction
- Data compression and visualization.
- Principal Component Analysis (PCA).
- Choosing the number of principal components.
- Applications in Recommender Systems.
8. Reinforcement Learning
- Markov Decision Process.
- Dynamic Programming.
- Reinforcement Learning algorithms.
9. Introduction to Deep Learning
- Introduction to Deep Forward Networks.
- Notes on Convolutional Neural Networks (CNN).
- Notes on Generative Adversarial Networks (GAN).
10. Case Studies and Projects
Various case studies will be presented and projects will be proposed in which to apply the notions learned on various domains of interest. In particular, the topics covered may concern, among other things, applications of ML methods and techniques in the following areas:
• Social Media Analysis (sentiment analysis, fake news detection, fake users detection, ecc.)
• Financial Machine Learning (algorithmic trading, ecc.)
• Recommender Systems (social RecSys, cultural heritage RecSys, e-commerce RecSys, ecc.)
• Data Science (prediction functions per applicazioni pratiche, ecc.)
• Computer Vision (object detection, face detection, face recognition, content-based video analysis, ecc.)
• Bioinformatics (recognition of genetic sequences, ecc.)
Mutuazione: 20810266 Machine Learning in Ingegneria informatica LM-32 MICARELLI ALESSANDRO, GASPARETTI FABIO
Programme
1. Introduction to the Course- Areas of interest in Machine Learning.
- Potenzialità dei modelli e dei metodi di ML.
2. Regression
- Introduction to Linear Regression.
- Overfitting in Regression.
- Regularization: Ridge Regression.
- Feature Selection and Lasso.
3. Classification
- Logistic Regression for classification.
- Overfitting in Classification.
- Boosting. AdaBoost algorithm.
- Naïve Bayes.
- Support Vector Machines.
4. Clustering
- Algoritmi k-means e k-means++ algorithms
- Expectation Maximization.
- Clustering gerarchico.
5. Artificial Neural Networks
- Architecture of Artificial Neural Networks.
- Backpropagation Learning algorithm.
- Applications of Artificial Neural Networks.
6. Keras and TensorFlow environments
- Keras and TensorFlow languages for developing ML applications.
- GPU-based architectures. Nvidia Tesla and Volta GPUs.
- Using TensorFlow with GPU support.
7. Dimensionality Reduction
- Data compression and visualization.
- Principal Component Analysis (PCA).
- Choosing the number of principal components.
- Applications in Recommender Systems.
8. Reinforcement Learning
- Markov Decision Process.
- Dynamic Programming.
- Reinforcement Learning algorithms.
9. Introduction to Deep Learning
- Introduction to Deep Forward Networks.
- Notes on Convolutional Neural Networks (CNN).
- Notes on Generative Adversarial Networks (GAN).
10. Case Studies and Projects
Various case studies will be presented and projects will be proposed in which to apply the notions learned on various domains of interest. In particular, the topics covered may concern, among other things, applications of ML methods and techniques in the following areas:
• Social Media Analysis (sentiment analysis, fake news detection, fake users detection, ecc.)
• Financial Machine Learning (algorithmic trading, ecc.)
• Recommender Systems (social RecSys, cultural heritage RecSys, e-commerce RecSys, ecc.)
• Data Science (prediction functions per applicazioni pratiche, ecc.)
• Computer Vision (object detection, face detection, face recognition, content-based video analysis, ecc.)
• Bioinformatics (recognition of genetic sequences, ecc.)
Prerequisiti
Computer Science Foundations.Core Documentation
Lecture notes by the professor.Type of delivery of the course
Lessons and exercises. Project supervision.Attendance
Attendance is not compulsory, but it is strongly recommended.Type of evaluation
Written test, practical test. teacher profile teaching materials
- Areas of interest in Machine Learning.
- Potenzialità dei modelli e dei metodi di ML.
2. Regression
- Introduction to Linear Regression.
- Overfitting in Regression.
- Regularization: Ridge Regression.
- Feature Selection and Lasso.
3. Classification
- Logistic Regression for classification.
- Overfitting in Classification.
- Boosting. AdaBoost algorithm.
- Naïve Bayes.
- Support Vector Machines.
4. Clustering
- Algoritmi k-means e k-means++ algorithms
- Expectation Maximization.
- Clustering gerarchico.
5. Artificial Neural Networks
- Architecture of Artificial Neural Networks.
- Backpropagation Learning algorithm.
- Applications of Artificial Neural Networks.
6. Keras and TensorFlow environments
- Keras and TensorFlow languages for developing ML applications.
- GPU-based architectures. Nvidia Tesla and Volta GPUs.
- Using TensorFlow with GPU support.
7. Dimensionality Reduction
- Data compression and visualization.
- Principal Component Analysis (PCA).
- Choosing the number of principal components.
- Applications in Recommender Systems.
8. Reinforcement Learning
- Markov Decision Process.
- Dynamic Programming.
- Reinforcement Learning algorithms.
9. Introduction to Deep Learning
- Introduction to Deep Forward Networks.
- Notes on Convolutional Neural Networks (CNN).
- Notes on Generative Adversarial Networks (GAN).
10. Case Studies and Projects
Various case studies will be presented and projects will be proposed in which to apply the notions learned on various domains of interest. In particular, the topics covered may concern, among other things, applications of ML methods and techniques in the following areas:
• Social Media Analysis (sentiment analysis, fake news detection, fake users detection, ecc.)
• Financial Machine Learning (algorithmic trading, ecc.)
• Recommender Systems (social RecSys, cultural heritage RecSys, e-commerce RecSys, ecc.)
• Data Science (prediction functions per applicazioni pratiche, ecc.)
• Computer Vision (object detection, face detection, face recognition, content-based video analysis, ecc.)
• Bioinformatics (recognition of genetic sequences, ecc.)
Mutuazione: 20810266 Machine Learning in Ingegneria informatica LM-32 MICARELLI ALESSANDRO, GASPARETTI FABIO
Programme
1. Introduction to the Course- Areas of interest in Machine Learning.
- Potenzialità dei modelli e dei metodi di ML.
2. Regression
- Introduction to Linear Regression.
- Overfitting in Regression.
- Regularization: Ridge Regression.
- Feature Selection and Lasso.
3. Classification
- Logistic Regression for classification.
- Overfitting in Classification.
- Boosting. AdaBoost algorithm.
- Naïve Bayes.
- Support Vector Machines.
4. Clustering
- Algoritmi k-means e k-means++ algorithms
- Expectation Maximization.
- Clustering gerarchico.
5. Artificial Neural Networks
- Architecture of Artificial Neural Networks.
- Backpropagation Learning algorithm.
- Applications of Artificial Neural Networks.
6. Keras and TensorFlow environments
- Keras and TensorFlow languages for developing ML applications.
- GPU-based architectures. Nvidia Tesla and Volta GPUs.
- Using TensorFlow with GPU support.
7. Dimensionality Reduction
- Data compression and visualization.
- Principal Component Analysis (PCA).
- Choosing the number of principal components.
- Applications in Recommender Systems.
8. Reinforcement Learning
- Markov Decision Process.
- Dynamic Programming.
- Reinforcement Learning algorithms.
9. Introduction to Deep Learning
- Introduction to Deep Forward Networks.
- Notes on Convolutional Neural Networks (CNN).
- Notes on Generative Adversarial Networks (GAN).
10. Case Studies and Projects
Various case studies will be presented and projects will be proposed in which to apply the notions learned on various domains of interest. In particular, the topics covered may concern, among other things, applications of ML methods and techniques in the following areas:
• Social Media Analysis (sentiment analysis, fake news detection, fake users detection, ecc.)
• Financial Machine Learning (algorithmic trading, ecc.)
• Recommender Systems (social RecSys, cultural heritage RecSys, e-commerce RecSys, ecc.)
• Data Science (prediction functions per applicazioni pratiche, ecc.)
• Computer Vision (object detection, face detection, face recognition, content-based video analysis, ecc.)
• Bioinformatics (recognition of genetic sequences, ecc.)
Prerequisiti
Computer Science Foundations.Core Documentation
Lecture notes by the professor.Attendance
Attendance is not compulsory, but it is strongly recommended.Type of evaluation
Written test, practical test.