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Universitas_Indonesia: CS204 Machine Learning Design Framework

Machine Learning Design Framework

Universitas_Indonesia
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About This Course

This course introduces the concept of machine learning architecture frameworks, providing solutions for common challenges in machine learning. The primary aim is to help learners understand and apply design frameworks in various aspects of machine learning, from data representation to model deployment. The course covers key topics like data preprocessing, model optimization, and ethical AI practices.

The course is self-paced, meaning learners can progress through the material at their own convenience. There will be no interactive discussions or live sessions. Instead, the course will consist entirely of video lectures, reading materials, and quizzes. The technologies used include an online platform for streaming videos, accessing reading resources, and completing quizzes.

Requirements

The intended audience for this MOOC consists of learners who have a beginner-level background in machine learning. This includes students and professionals who have some foundational understanding of machine learning but are looking to build further knowledge in applying design frameworks to solve common machine learning problems. No advanced programming or machine learning experience is required, though familiarity with basic concepts like data handling and simple models is beneficial.

Course Instructure

Ari Wibisono is a scholar in the Faculty of Computer Science at Universitas Indonesia. With a  contribution to the fields of parallel, grid, and cloud systems, his research interests also encompass intelligent systems and big data. Ari Wibisono has authored numerous influential papers, including works on traffic big data prediction and vehicle speed measurement using advanced algorithms. His efforts have earned him over 700 citations, reflecting his impact on the academic community.

In addition to his research, Ari Wibisono is actively involved in projects that apply machine learning and data analytics to real-world problems. Ari Wibisono's expertise in both theoretical and applied aspects of computer science. Ari Wibisono has been involved in various teaching activities, contributing to the development of students in Data Analytics and Infrastructure Information Management  fields. His commitment to education is reflected in his extensive teaching history, helping to shape future professionals in the ever-evolving tech landscape.

email: ari.w@cs.ui.ac.id 

Frequently Asked Questions

Why is this particular course a good candidate for development as a MOOC?

This course is a good candidate for development as a MOOC because it addresses a key gap in machine learning education by focusing on practical design frameworks that are essential for building scalable and maintainable machine learning systems. Design frameworks are widely applicable across industries and help learners solve common ML challenges efficiently. By offering this course in a self-paced, online format, it can reach abroad audience of learners, from beginners with foundational knowledge to professionals seeking structured, scalable solutions in machine learning.

How will this course take advantage of the scale, reach, or other affordances of the MOOC platforms?

This course will leverage the scale and reach of MOOC platforms by providing free access to learners around the world, enabling them to acquire valuable knowledge in machine learning without geographical or financial barriers. The self-paced format allows flexibility, enabling learners to engage with the material at their convenience, which is essential for busy professionals and students. Additionally, MOOC platforms support global engagement with features like quizzes, automated grading, and content delivery to large numbers of participants simultaneously.

How will this course engage a diverse and global audience?

The course is designed to cater to a global audience by addressing universal challenges in machine learning that are relevant across different sectors such as healthcare, finance, and technology. The material is delivered in English, the predominant language for global education, and is accessible to learners with a beginner-level background in machine learning, making it suitable for abroad range of participants. The self-paced structure ensures that learners from different time zones and with varying schedule scan participate effectively.

How will developing this course contribute to UI’s globalization, course sharing with other universities, and C-HUB?

Developing this course will enhance Universitas Indonesia’s (UI)globalization efforts by expanding its presence in the international education space through MOOCs. It will position UI as a leader in delivering practical, industry-relevant machine learning content to a global audience, which aligns with the goals of C-HUB’s course-sharing initiatives. This course could also be shared with other universities as part of UI’s collaborative efforts, facilitating academic exchange and global partnerships.

What is your expertise in the course topic?

My expertise in machine learning and architecture are demonstrated through extensive experience in teaching and working with machine learning systems. I have a deep understanding of the core principles of data representation, modelbuilding, and deployment strategies, and have applied machine learning in various real-world applications. Additionally, I have worked with design frameworks that help address scalability, reproducibility, and ethical concerns in machine learning projects, making me well-qualified to deliver this course.https://scholar.google.com/citations?user=Nms5vioAAAAJ&hl=en&oi=ao

How How does the course differ from existing classes offered by MOOC providers?

While many MOOCs cover general machine learning concept sand algorithms, this course is distinct because it focuses specifically on machine learning design frameworks, which are practical, reusable solutions for common challenges in ML development. Most existing courses tend to focus on theoretical aspects or individual models, whereas this course takes a solution-oriented approach that is immediately applicable to real-world machine learning problems. This approach provides learners with hands-on strategies to improve scalability, reproducibility, and model maintenance.

Describe any MOOCs you have participated in, as an instructor or a learner.

As a learner, I have participated in several MOOCs related to machine learning and data science, including courses on platforms like Coursera and edX, where I gained insights into advanced machine learning techniques. As an instructor, I have extensive experience in teaching in-person and online courses on machine learning at the university level, giving me the pedagogical foundation needed to design effective and engaging online content.

Course Summary

  1. Course Number

    CS204

  2. Classes Start

  3. Classes End

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