The course aims to provide advanced and specific competencies in recent machine learning models and technologies. The course will enable the solving of complex problems through appropriate problem formulation and definition of the most suitable models and knowledge representations, and the most efficient implementation techniques for machine learning algorithms. Reinforcement learning and state-of-the-art models, such as graph neural networks and tuning and self-tuning techniques, will be introduced.
The course consists of a theoretical and methodological part on advanced and innovative concepts, and a laboratory activity in which these concepts are applied in problem solving using the latest development frameworks.
The course consists of a theoretical and methodological part on advanced and innovative concepts, and a laboratory activity in which these concepts are applied in problem solving using the latest development frameworks.
Curriculum
scheda docente
materiale didattico
- 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.)
Programma
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.)
Testi Adottati
Lecture notes by the professor.Bibliografia Di Riferimento
S.J.Russel, P.Norvig "Artificial Intelligence: A Modern Approach", 4/Ed (2020). Pearson Education.Modalità Erogazione
Lessons and exercises. Project supervision.Modalità Frequenza
Attendance is not compulsory, but it is strongly recommended.Modalità Valutazione
Written test, project evaluation.
scheda docente
materiale didattico
- 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.)
Programma
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.)
Testi Adottati
Lecture notes by the professor.Bibliografia Di Riferimento
S.J.Russel, P.Norvig "Artificial Intelligence: A Modern Approach", 4/Ed (2020). Pearson Education.Modalità Erogazione
Lessons and exercises. Project supervision.Modalità Frequenza
Attendance is not compulsory, but it is strongly recommended.Modalità Valutazione
Written test, project evaluation.
scheda docente
materiale didattico
- 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.)
Programma
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.)
Testi Adottati
Lecture notes by the professor.Bibliografia Di Riferimento
S.J.Russel, P.Norvig "Artificial Intelligence: A Modern Approach", 4/Ed (2020). Pearson Education.Modalità Erogazione
Lessons and exercises. Project supervision.Modalità Frequenza
Attendance is not compulsory, but it is strongly recommended.Modalità Valutazione
Written test, project evaluation.
scheda docente
materiale didattico
- 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.)
Programma
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.)
Testi Adottati
Lecture notes by the professor.Bibliografia Di Riferimento
S.J.Russel, P.Norvig "Artificial Intelligence: A Modern Approach", 4/Ed (2020). Pearson Education.Modalità Erogazione
Lessons and exercises. Project supervision.Modalità Frequenza
Attendance is not compulsory, but it is strongly recommended.Modalità Valutazione
Written test, project evaluation.