S317 Data Science Laboratory with Python
| Lecturer: | Prof. Luca Romeo, PhD (Tenure Track Assistant Professor of Computer Science with University of Macerata) |
| Course description: | S317 |
| Language: | English |
| Recommended for this semester or higher: | 5 |
| ECTS-Credits: | 6 |
| Course can be taken as part of following programs/modules: | For more information please refer to alma. |
| Prerequisite for: | --- |
| Prerequisites: | Previous preliminary knowledge in basic programming is required (either R or Python), as well as basic knowledge in statistics. |
| Limited attendance: | 25 Registration: Please see below for details. |
| Course Type: | Lecture - Hybrid Course (mix in-person and remote participation) |
| Date: | Lecture: on Thursdays from 8am c.t. - 10am - (Beginning April 18, 2024) Please note this is not a fully online course. |
| Registration: | Limited to 25 participants. Registration open for all (no first-come, first-served): Registration will open on February 1, 2024 on alma - end of registration time: Sunday, April 14, 2024). If the number of applications exceeds the number of places available (25), a random selection will be made from all the applications received. Notice of admission or non-admission on Monday, April 15 by email. |
| Downloads: | in ILIAS |
| Method of Assessment: | Oral Exam: Presentation, Discussion of the Project 30% |
| Content: | This course offers a practical immersion into the dynamic field of applied data science using Python, delivering a cutting-edge perspective on data analysis. Participants will gain hands-on experience and some theoretical foundation in Artificial Intelligence, Deep Learning, and Machine Learning. The curriculum emphasizes the latest advancements in data analysis methodologies, with a primary focus on applying machine learning models to diverse benchmark datasets. By the end of the course, students will be equipped with the skills and insights needed to navigate the evolving landscape of data science, empowering them to tackle real-world challenges with confidence. |
| Objectives: | - Knowledge and Understanding: This course enhances students' comprehension of fundamental machine learning principles and their practical application in Python. - Applying Knowledge and Understanding: Students will cultivate the capacity to independently and collaboratively execute machine learning projects using Python. - Communication Skills: Through hands-on experience in resolving pattern recognition challenges with various Python libraries, students will refine their communication skills. Subsequently, they will demonstrate their interdisciplinary competence through reports and group presentations. - Learning Skills: The course actively contributes to the development of students' problem-solving abilities. Participants will acquire the skills to devise solutions by implementing predictive models for addressing benchmark tasks. |
| Literature: | Book: Pattern Recognition and Machine Learning (Information Science and Statistics) 0387310738 I Springer-Verlag A Christopher M. Bishop 2006 |