HIGH PERFORMANCE COMPUTING
- Teaching in italian
- HIGH PERFORMANCE COMPUTING
- HIGH PERFORMANCE COMPUTING
- Subject area
- Reference degree course
- COMPUTER ENGINEERING
- Course type
- Master's Degree
- Teaching hours
- Frontal Hours: 81.0
- Academic year
- Year taught
- Course year
- PERCORSO COMUNE
- Reference professor for teaching
- ACCARINO GABRIELE
- Linear Algebra calculus
- Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program
- Programming skills (any programming language and paradigm)
- Python programming skills
- Data handling and preparation techniques
- Knowledge of data science and visualization Python libraries (numpy, sklearn, pandas, scipy, matplotlib)
The course provides a broad introduction to Machine Learning theory and practice for supervised learning applications.
- Linear regression (univariate/multivariate)
- Normal equation
- Gradient descent algorithm
- Polinomial regression
- Regularization techniques (Ridge/Lasso)
- Trade-off between high bias and high variance
- Logistic regression (univariate/multivariate; single class/multi class)
- Artificial Neural Networks and back-propagation
- Design and implementation of an Artificial Neural Network in the Keras Deep Learning framework
Hands-on are also organized to provide students the capacity to develop specific use cases, using the Python programming language and the Jupiter Notebook environment.
KNOWLEDGE AND UNDERSTANDING:
By the end of the course, students will gain knowledge of the key theoretical and practical in the context of Machine Learning.
APPLYING KNOWLEDGE AND UNDERSTANDING:
By the end of the course, students will know how to use the acquired notions for the efficient use to design and implement Machine Learning algorithms for both regression and classification tasks.
By the end of the course, students will be able to critically assess the different Machine Learning approaches depending on the specific problem, being also able to assess their goodness.
By the end of the course, students will be able to use a clear language and an adequate scientific terminology to argue on the topics dealt with in the course.
Students will be able to classify, schematize, summarize and process the acquired knowledge. By the end of the course, students will have the appropriate skills to develop and widen their knowledge of Machine Learning, with particular regard to the use of reference documentation and other information available online.
The credits of this course are obtained by means of an oral exam, that will assess the overall learning results achieved by the student.
Students (both attending and non-attending) will be asked three questions, one of which is aimed at checking problem solving skills and the student's ability to use the theoretical and practical notions acquired.
The final score will consider:
• The level of theoretical/practical notions acquired (50%);
• The ability to use the theoretical/practical notions acquired (30%);
• The ability to make autonomous assessments (10%);
• The communication skills (10%).
Honours are awarded to students mastering the topics covered in the course.
It is important to notice that, students interactions throughout the course will be also taken into consideration in the final evaluation process.
The oral exam is aimed at verifying to what extent the student has gained knowledge and understanding of the selected topics of the course and is able to communicate about his understanding. Students, divided into small groups, will also get hands-on experience, developing small projects on specific topics of the course. The max final vote is expressed as 30/30 with the possibility to get the laude.
- Machine Learning course by Andrew Ng (Stanford)
- Goodfellow, I. et al., 2016. Deep Learning
Second Semester (dal 01/03/2022 al 10/06/2022)
Type of assessment
Oral - Final grade