- Course: Applied Programming Systems
- Year: 2014 - 2015
- School: Technological School of Electronic Systems, Sofia
- Instructor: Dimitar Nikolov, email
- Need help?
In this course you will learn best practices in Matlab/Octave and basic introduction to machine learning algorithms. Topics include MatLab fundamentals e.g. vector, matrices, data types, scripts etc., functions, 2D and 3D graphics. Discover the intriguing world of the supervised and unsupervised learning with linear regression, k-NN, clustering and neural networks. Create a classifier to map unknown pictures to a category.
Computers are provided in the lab, though you are encouraged to bring a laptop for in-class exercises.
- Understanding of variables, data types, control flow, and basic function usage
- Strong mathmatical skills and ability to identify patterns or links between seemingly unrelated issues
These won't be enforced by the instructor, but you will be pretty lost without understanding those concepts.
All assignments are listed within the Course Outline.
- Fork the specific homework repository (found under github.com/ppstues)
- Clone the repository to your computer
- Create and/or modify the files to complete your solution
- Check your solution if matches the required exercise
- Make sure all of your code is committed
- Push/sync up to GitHub
- Create a pull request on the original repository by the due time (generally the day before start of the following class)
- You can continue to push fixes and improvements until the close date (listed in Classes) – just add a comment in the pull request to let me know it's been updated.
Feedback will be given in the pull request, so please respond with your thoughts and questions! You are welcome to open the pull request as the work is still in-progress if you are stuck and want to ask a question – just mention @nikolovd with the question to make sure I know to look at it sooner.
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- Git and GitHub
- GitHub Pages
Please respect the terms of use and/or license of any code you find, and if duplicate an algorithm or code from elsewhere, credit the original source with an inline comment.