人工智慧：機器學習與理論基礎 (Artificial Intelligence - Learning & Theory)

National Taiwan University via Coursera

Go to Course: https://www.coursera.org/learn/ai2

Introduction

### Course Review: 人工智慧：機器學習與理論基礎 (Artificial Intelligence - Learning & Theory) In the dynamic realm of Artificial Intelligence, the course "人工智慧：機器學習與理論基礎" (Artificial Intelligence - Learning & Theory) stands out as an indispensable resource for anyone eager to delve into the complexities of machine learning. Offered on Coursera, this course is meticulously structured to impart foundational knowledge and practical insights into machine learning technology that is intertwined with AI. #### Course Overview The course is aptly divided into two main parts, with a significant focus on machine learning, a pillar of modern AI applications. Exploring theoretical frameworks developed as far back as the 1990s, including the pivotal VC (Vapnik-Chervonenkis) theory, students will gain a robust understanding of the principles that govern contemporary machine learning practices. Key topics covered include: - **Fundamental Theories**: The backbone of machine learning concepts, laying the groundwork for advanced applications. - **Classification Techniques**: Insight into various classifiers, including decision trees and support vector machines, enhancing the learner’s problem-solving toolkit. - **Neural Networks and Deep Learning**: An exploration of the architectures powering recent advancements in AI, focusing on both shallow learning frameworks and the evolution towards deep architectures. - **Reinforcement Learning**: Addressing one of the most exciting areas in AI, including deep reinforcement learning, which applies to real-world scenarios. This course encapsulates technologies that date back to the 1950s, while also incorporating the latest developments as of 2016, making it both a historic and contemporary guide to machine learning. #### Core Objectives The course aims to arm students with: 1. A foundational understanding of machine learning concepts related to artificial intelligence. 2. Knowledge of basic theoretical principles, classification algorithms, neural networks, and reinforcement learning methods. 3. Practical skills that allow learners to apply these techniques to their own challenges, bridging the gap between theory and application. #### Prerequisites To ensure participants can fully engage with the course material, a basic background in computer science is required. Knowledge of data structures and algorithms is recommended but not mandatory, providing flexibility to a broader audience. #### Syllabus Overview The course curriculum includes five key areas: 1. **Concept Learning**: Introduction to the various types of learning models and their foundational theories. 2. **Computational Learning Theory**: Insights into how learning algorithms operate under different circumstances. 3. **Classification**: Exploration of methods for sorting data and making predictions based on identified patterns. 4. **Neural Networks and Deep Learning**: Deep dive into the architecture and functionality of neural networks, emphasizing deep learning techniques. 5. **Reinforcement Learning**: Understanding the principles of agents interacting with their environment to maximize performance and learning outcomes. #### Final Thoughts and Recommendation This course is highly recommended for both newcomers and those with some prior exposure to machine learning or computer science. The clarity of instruction, paired with the depth of knowledge offered, equips learners with both theoretical and practical skills necessary to navigate the rapidly evolving field of AI. Enrolling in "人工智慧：機器學習與理論基礎" will not only enhance your understanding of machine learning but will also empower you to address real-world problems using AI technologies. Whether you're considering a career in AI, looking to fuse these skills with your current profession, or simply passionate about learning, this course is a valuable investment in your future. Overall, if you aspire to deepen your expertise in artificial intelligence and understand machine learning in a structured manner, don’t hesitate to enroll!

Syllabus

Concept learning

Computational Learning Theory

Classification

Neural Network and Deep learning

Reinforcement learning