- The goal of the course is to provide a basic understanding of the machine learning (ML) and deep learning (DL) concepts.
Course objectives:
To provide a capability:
- Python programming for ML and DL.
- Conceptual understanding of:
- Data
- Models
- Loss functions
- Metrics
- Model training and evaluation.
This course is expected to have a follow-up course on signals prediction and classification.
Grading policy
The course grade is based on homework assignments (100%)
Homework
- Coding assignments in Matlab.
- Submission via the Moodle page.
- The homeworks are graded based on the following criteria:
- Solution correctness (25%)
- Code quality (25%)
- Explanation clarity (25%)
- Formatting (25%): special attention is required for the following:
- Plots are required to be labeled and have a title
- Headings for each section
- Submissions should include a LiveScript notebook and corresponding PDF file.
Special notes:
- This is a new course that is under development.
- The course is designed to be self-contained (i.e., no formal prerequisite courses are required), although the basic knowledge of probability and linear algebra is assumed.
- The students are expected to use Matlab LiveScript for the course assignments. Matlab version 2024a is recommended.