Pasquale FOGGIA | MACHINE LEARNING
Pasquale FOGGIA MACHINE LEARNING
cod. 0622700066
MACHINE LEARNING
| 0622700066 | |
| DIPARTIMENTO DI INGEGNERIA DELL'INFORMAZIONE ED ELETTRICA E MATEMATICA APPLICATA | |
| EQF7 | |
| COMPUTER ENGINEERING | |
| 2020/2021 |
| OBBLIGATORIO | |
| YEAR OF COURSE 1 | |
| YEAR OF DIDACTIC SYSTEM 2017 | |
| PRIMO SEMESTRE |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| ING-INF/05 | 3 | 24 | LESSONS | |
| ING-INF/05 | 3 | 24 | EXERCISES | |
| ING-INF/05 | 3 | 24 | LAB |
| Objectives | |
|---|---|
| THE COURSE IS AIMED AT PROVIDING THE STUDENT WITH THE THEORETICAL, METODOLOGICAL AND TECHNOLOGICAL KNOWLEDGE ON MACHINE LEARNING AND ON THE ANALYSIS OF LARGE DATA SETS, INCLUDING BOTH TRADITIONAL TECHNIQUES AND INNOVATIVE PARADIGMS SUCH AS DEEP LEARNING. KNOWLEDGE AND UNDERSTANDING PARADIGMS OF STRUCTURAL LEARNING, STATISTICAL LEARNING AND NEURAL LEARNING. UNSUPERVISED LEARNING. DEEP LEARNING. PARADIGMS AND TOOLS FOR BIG DATA ANALYSIS. APPLIED KNOWLEDGE AND UNDERSTANDING DESIGN AND REALIZATION OF SOLUTIONS TO LEARNING AND DATA ANALYTICS PROBLEM BY INTEGRATING EXISTING TOOLS AND TUNING IN AN EFFECTIVE WAY THEIR OPERATING PARAMETERS. |
| Prerequisites | |
|---|---|
| THE COURSE REQUIRES BASIC KNOWLEDGE OF THE PYTHON PROGRAMMING LANGUAGE. |
| Contents | |
|---|---|
| Introduction to machine learning. Bias and variance errors. Overfitting. The curse of dimensionality. Bayesian and NN classifiers. Linear classifiers. SVM. Kernel trick. Linear regression and regularization techniques. Logistic regression. Clustering algorithms. Multiclassifier systems. Random forests. Reinforcement learning. Q-Learning. Introduction to Hidden Markov Models. Artificial Neural Networks. Perceptrons. MLP. Clustering with neural networks. LVQ. Manifold learning and Self Organizing Maps. Deep learning. Convolutional neural networks. Recurrent networks. LSTM and GRU. Advanced deep learning architectures. Autoencoders. Simultaneous detection and recognition. YOLO. Generative-Adversarial Networks. |
| Teaching Methods | |
|---|---|
| THE COURSE CONTAINS THEORETICAL LECTURES, IN-CLASS EXERCITATIONS AND PRACTICAL LABORATORY EXERCITATIONS. |
| Verification of learning | |
|---|---|
| THE EXAM IS COMPOSED BY THE DISCUSSION OF A TEAM PROJECTWORK (FOR 3-4 PERSONS TEAMS) AND AN ORAL INTERVIEW. THE DISCUSSION OF THE PROJECTWORK AIMS AT EVALUATING THE ABILITY TO BUILD A SIMPLE APPLICATION OF THE TOOLS PRESENTED IN THE COURSE TO A PROBLEM ASSIGNED BY THE TEACHER, AND INCLUDES A PRACTICAL DEMONSTRATION OF THE REALIZED APPLICATION, A PRESENTATION OF A QUANTITATIVE EVALUATION OF THE APPLICATION PERFORMANCE AND A DESCRIPTION OF THE TECHNICAL CHOICES INVOLVED IN ITS REALIZATION. THE INTERVIEW EVALUATES THE LEVEL OF THE KNOWLEDGE AND UNDERSTANDING OF THE THEORETICAL TOPICS, TOGETHER WITH THE EXPOSITION ABILITY OF THE CANDIDATE. |
| Texts | |
|---|---|
| Lecture notes and other material provided during the course |
| More Information | |
|---|---|
| COURSE LANGUAGE IS ENGLISH. |
BETA VERSION Data source ESSE3 [Ultima Sincronizzazione: 2022-05-23]
