| MATHEMATICS (TURKISH, PHD) | |||||
| PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| VCD4151 | Machine Learning for Artists and Designers | Fall | 2 | 2 | 3 | 5 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| Type of course: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Dr. Öğr. Üyesi İPEK TORUN |
| Course Objectives: | This course introduces students to current multimedia and new media technologies and techniques. Course will start with various discussions on where the technology leads art and communication. Then, will move on the practical applications with brief introductions to a wide array of softwares. Various topics of discussions would be the role of Machine Learning in creative role, Neural Aesthetic, Data Visualisation and their application during the process of artistic output. Practical applications would be; Wekinator for building interactive systems, Python for neural aesthetic applications like Style Transfer and introduction to Processing. Topics and ideas such Using OSC to sync softwares like Ableton Live and Resolume, interaction of social media would be additional subject matters. |
|
The students who have succeeded in this course; 1) General information about Machine Learning 2) Machine Learning in the world of communication design 3) Algorithms and Algorithmic Design 4) Simple Python 5) Information on Neural Networks 6) Information on making art with Neural Networks 7) Information on how big companies like Facebook and google use Machine Learning 8) Wekinator 9) Applications of: Making music 10) Applications of: Design 11) Applications of: Drawing 12) Applications of: Generating text 13) t-SNE |
| Machine Learning basics, neural networks [ANN, RNN, CNN], GANs, classification algorithms, practical uses of Machine Learning, artistic use of Machine Learning, google Magenta, text generation, NSynth, style transfer, t-SNE, Simple Python, Tensorflow, Wekinator, Deep Learning, Introduction to AI, how and where big companies like Facebook and google uses Machine Learning |
| Week | Subject | Related Preparation | |
| 1) | Meeting and talking about the course in general. Showing contemporary examples of new media works. Overview of softwares and possible outcomes of the course. Introduction to a Google Drive folder where the students can find the required softwares and files. | ||
| 2) | What is machine learning and where it stands in the world of art and communication today? Discussion how the machine intelligence might and (already is) is changing the way we communicate and produce. Discussion: Practical use & Creative use | ||
| 3) | Examples of modern usage (Facebook, google etc...) Some examples of machine learning art pieces. Simple logic behind Machine Learning = Apples & Oranges What is an algorithm? (Example with Markov Chains) Hardcoding vs algorithm | ||
| 4) | How to get started? Supervised / Unsupervised learning models Machine Learning Art | ||
| 5) | Introduction to Wekinator What is OSC (Open Sound Control) ? Basic applications of OSC (An example with Resolume & Ableton Live) Examples of interactive design. | ||
| 6) | Introduction to Neural Networks Most basic example: MNIST Examples of modern usage (Google Search etc...) What is Deep Learning? | ||
| 7) | Discussion: Thoughts so far First ideas on projects Q&A | ||
| 8) | Data visualisation t-SNE Difference of data visualisation and information design. Brief talk about Processing | ||
| 9) | Discussion: Artistic endeavour and idea of software Discussion of students ideas on what they want to work on More examples of New Media works and ML artworks. | ||
| 10) | Project Critiques | ||
| 11) | Project Critiques | ||
| 12) | Project Critiques | ||
| 13) | Project Critiques | ||
| 14) | Project Critiques | ||
| Course Notes: | Ders notları öğretim elemanı tarafından derslerde iletilir. Course notes distributed on class by the instructor. |
| References: | 1. C.H. Edwards,Jr. David E. Penney, Calculus with Analytic Geometry, Prentice- Hall Englewood Cliffs, New Jersey. 2. Richard A.Silverman, Calculus with Analytic Geometry, Prentice- Hall Englewood Cliffs, New Jersey |
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | % 0 | |
| Laboratory | % 0 | |
| Application | % 0 | |
| Field Work | % 0 | |
| Special Course Internship (Work Placement) | % 0 | |
| Quizzes | % 0 | |
| Homework Assignments | % 0 | |
| Presentation | % 0 | |
| Project | 2 | % 45 |
| Seminar | % 0 | |
| Midterms | % 0 | |
| Preliminary Jury | % 0 | |
| Final | 1 | % 55 |
| Paper Submission | % 0 | |
| Jury | % 0 | |
| Bütünleme | % 0 | |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 0 | |
| PERCENTAGE OF FINAL WORK | % 100 | |
| Total | % 100 | |
| Activities | Number of Activities | Duration (Hours) | Workload |
| Course Hours | 14 | 4 | 56 |
| Laboratory | 0 | 0 | 0 |
| Application | 14 | 2 | 28 |
| Special Course Internship (Work Placement) | 0 | 0 | 0 |
| Field Work | 0 | 0 | 0 |
| Study Hours Out of Class | 14 | 1 | 14 |
| Presentations / Seminar | 0 | 0 | 0 |
| Project | 2 | 10 | 20 |
| Homework Assignments | 0 | 0 | 0 |
| Quizzes | 0 | 0 | 0 |
| Preliminary Jury | 0 | 0 | 0 |
| Midterms | 0 | 0 | 0 |
| Paper Submission | 0 | 0 | 0 |
| Jury | 0 | 0 | 0 |
| Final | 1 | 10 | 10 |
| Total Workload | 128 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |