Study Guide

Objective

After completing the course, the student can:
- describe basic concepts and processes related to Data Engineering and AI

Content

- Data Engineering process
- Basics of AI
- Fields and evolution of AI
- Big data
- Basics of Machine Learning

Materials

Course book:

Aurélien Géron.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
2nd Edition.
Publisher : O'Reilly Media; 2nd edition
(October 15, 2019)

The course book can be read in electronic form from our institution's eBook Central database.

The course also has other materials, which will be announced during the course.

Exam schedules

No exam

Student workload

Contact hours:
- 4 times 3h theory and practice: 4 x 4h = 12 hours

Assignments for the final project: approximately 118 hours

Total: approximately: 130 hours

Content scheduling

Course Outline and Schedule:
Week 37: Introduction to the Course and Data Preprocessing:
• Project overview and requisites
• Initiation of project's initial phase: Data preprocessing and visualization (assignment 1)
Week 41: Machine Learning I
• Students' presentations showcasing outputs and implementation of the first assignment
• Commencement of phase two: Supervised and unsupervised learning (assignment 2)
Week 45: Machine Learning II
• Students' presentations showcasing outputs and implementation of the second assignment
• Tuning and optimizing your Machine Learning algorithms (assignment 3)
Week 49: Students' presentations showcasing outputs and implementation of the third assignment, followed by discussions encompassing all project phases.

Evaluation scale

H-5

Assessment methods and criteria

The course is graded on a scale of 0-5.
*
In order to receive an approved performance and pass the course, the student must receive an acceptable mark for the three assignments.
*
You can get at most 40, 30, and 30 points for each phase of the project (assignments 1-3). You can therefore get a maximum of 100 points from all phases of the project.

Assessment criteria, fail (0)

Less than 50 points in assignments not passed.

Assessment criteria, satisfactory (1-2)

50 - 65 points from the total points of the assignments

Assessment criteria, good (3-4)

66 - 80 points from the total points of the assignments

Assessment criteria, excellent (5)

81- 100 points from the total points of the assignments