Machine Learning
An overview of the techniques that are transforming many industries and will change our lives.
| Provided By | Politecnico di Milano
 |
| Type of provider | HE Institution |
| Provided at | https://www.pok.polimi.it/ |
| Learning opportunity type | MOOC |
| Language | en |
| Home page | https://www.pok.polimi.it/courses?search_query=AI105 |
| Start date | 2022-02-21 |
| End date | 2023-01-22 |
| Duration | 11 |
| Workload in hours | 8 |
| Admission procedure | Open to all |
| Price details | You can access the course absolutely free of charge. |
| Assessments | The final grade for the course is based on results from your responses to the quizzes you will find at the end of each week (weekly quizzes). You will successfully complete the course if you reach 60% (or more) of the total score by the end of the edition. The course’s total score will be calculated by averaging the scores of the assessed quizzes for each individual week. |
| Type of credential | Certificate of participation |
| ISCEDF Code | 0619 - Information and Communication Technologiesn.e.c. |
| Education subject | 061 Information and Communication Technologies (ICTs) 0619 Information and Communication Technologies not elsewhere classified 071 Engineering and engineering trades 0714 Electronics and automation |
| Learning settings | non formal learning |
| Learning outcome | Machine learning – LO – Classify machine Classify machine learning problems |
| Learning outcome type | Skill |
| Reusability level | Sector specific skills and competences |
| Related skill | http://data.europa.eu/esco/skill/8369c2d6-c100-4cf6-bd83-9668d8678433 |
| Learning outcome | Machine learning – LO – Classify supervised Classify supervised learning problems |
| Learning outcome | Machine learning – LO – Classify unsupervised Classify machine learning problems in unsupervised learning |
| Learning outcome | Machine learning – LO – Describe how |
| Learning outcome | Machine learning – LO – Describe the limitations Describe the limitations of machine learning techniques in supervised learning |
| Learning outcome type | Knowledge |
| Reusability level | Sector specific skills and competences |
| Related skill | http://data.europa.eu/esco/skill/e465a154-93f7-4973-9ce1-31659fe16dd2 |
| Learning outcome | Machine learning – LO – Describe the main Describe the main techniques for identifying clusters of data |
| Learning outcome | Machine learning – LO – Describe the utility Describe the utility of dimensionality reduction techniques |
| Learning outcome | Machine learning – LO – Explain Explain what a value function is and how it can be estimated using reinforcement learning |
| Learning outcome | Machine learning – LO – Formulate Formulate a sequential decision-making problem |
| Learning outcome | Machine learning – LO – Identify Identify the key elements of supervised learning algorithms |
| Learning outcome type | Knowledge |
| Reusability level | Cross-sector skills and competences |
| Related skill | http://data.europa.eu/esco/skill/54924a2c-daca-40d3-9716-4b38ceb04f38 |
| Learning outcome | Machine learning – LO – Perform Perform model evaluation and selection in supervised learning |
| Assessor type | artificial intelligence |
| Assessment format | automatic grading |
| Awarding opportunity | The Certificate of Accomplishment will be released to anyone who successfully completes the course by answering correctly to at least 60% of the questions by the end of the edition. You will be able to download the Certificate of Accomplishment directly on the website. Once you have successfully passed the course, you can request the Certificate of Accomplishment without waiting for the end of the edition. The Certificate of Accomplishment does not confer any academic credit, grade or degree. |
