Artificial Intelligence Course in Bahasa Indonesia

IMPORTANT NOTICE:
Indonesia has been one among a lot of countries around the world that is badly affected by Corona virus. The spreading of the virus is so fast with exponential growth. I believe that artificial intelligence can help people all around the world to fight against Corona virus. Therefore, to speed up AI research in fighting the virus–particularly in Indonesia, I have shared some teaching materials from my AI class taught in undergraduate level with Bahasa Indonesia. Please feel free to use and to share them among your colleagues. I hope this small contribution will promote AI research to fight Corona virus. 

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PRE-REQUISITE MATERIALS

MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning

Linear algebra concepts are key for understanding and creating machine learning algorithms, especially as applied to deep learning and neural networks. This course reviews linear algebra with applications to probability and statistics and optimization–and above all a full explanation of deep learning.

Playlist:
https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k

A. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
A very short introductory course about artificial intelligence technology ranging from the beginning of Turing Machine (1948) up to current speech recognition technology of Google Duplex (2018). If you are new to this field, I recommend you to watch these videos to get a grasp on what is artificial intelligence and how this technology has been developed.

Part 1: Industrial Revolution 4.0 and Artificial Intelligence
Part 2: History of Turing Machine
Part 3: Machine Learning: Supervised and Unsupervised
Part 4: Introduction to Deep Neural Network
Part 5: References for AI research

Download teaching materials:
https://www.slideshare.net/macsuntzu/introduction-to-artificial-intelligence-pengenalan-kecerdasan-buatan

Part 1: Industrial Revolution 4.0 and Artificial Intelligence
Part 2: History of Turing Machine
Part 3a: Machine Learning: Supervised and Unsupervised
Part 3b: Machine Learning: Supervised and Unsupervised
Part 4a: Introduction to Deep Neural Network
Part 4b: Introduction to Deep Neural Network
Part 5: References for AI research

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B. A FIRST COURSE IN MACHINE LEARNING
An introductory course in machine learning for layman. In this course, I will explain some basic machine learning algorithms such as linear regression, naive bayes, decision tree, clustering, and association rules. Furthermore, I will explain about some basic terminologies in machine learning field.

Video 01: Introduction to Machine Learning
Video 02: Linear Regression and Model Validation
Video 03: Rule-Based Machine Learning (Part 1)
Video 04: Rule-Based Machine Learning (Part 2)
Video 05: Probabilistic Machine Learning (Part 1)
Video 06: Probabilistic Machine Learning (Part 2)
Video 07: Probabilistic Machine Learning (Part 3)
Video 08: Clustering and Association Rules

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C. LEARNING DEEP LEARNING: DIGGING DEEPER
An exhaustive course about deep learning, starting from its historical point of view, its main difference with traditional machine learning, an algorithmic overview of deep learning (based on Brandon Rohrer’s course in 2017) and mathematics of deep learning (based on Alexander Amini’s course in 2019).

Video 01: The Rise of Artificial Intelligence Through Deep Learning (guest lecture by Professor Dr. Yoshua Bengio)
Video 02: Deep Learning Crash Course (guest lecture by Dr. Andrew Glassner)
Video 03: The History of Deep Learning (Dr. Sunu Wibirama)
Video 04: Machine Learning vs. Deep Learning
Video 05: Basic Principles of Deep Learning (Visually Explained). Slides are based on presentation by Brandon Rohrer (2017).
Video 06: Mathematics of Deep Learning (Perceptron). Slides are under MIT License © Alexander Amini and Ava Soleimany MIT 6.S191: Introduction to Deep Learning
Video 07: Mathematics of Deep Learning (Neural Network). Slides are under MIT License © Alexander Amini and Ava Soleimany MIT 6.S191: Introduction to Deep Learning
Video 08: Mathematics of Deep Learning (Gradient Descent dan Learning Rate). Slides are under MIT License © Alexander Amini and Ava Soleimany MIT 6.S191: Introduction to Deep Learning
Video 09: Basic Principles of Convolutional Neural Network

Download teaching materials:
PDF of lecture notes (6 MB)

More References:
1. Data Science for Business
2. Numsense! Data Science for the Layman: No Math Added
3. Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence
4. Introduction to Deep Learning (MIT Course / MIT 6.S191) by Alexander Amini and Ava Soleimany.