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人工智慧：機器學習與理論基礎 (Artificial Intelligence - Learning & Theory)
于天立
于天立
本課程第二部分著重在和人工智慧密不可分的機器學習。課程內容包含了機器學習基礎理論（包含 1990 年代發展的VC理論）、分類器（包含決策樹及支援向量機）、神經網路（包含深度學習）及增強式學習（包含深度增強式學習。
此部份技術包含最早追溯至 1950 年代直到最近 2016 年附近的最新發展。此課程從基礎理論開始，簡介了各機器學習主流技法以及從淺層學習架構演變到最近深度架構的轉換。
本課程之核心目標為：
Read more
本課程第二部分著重在和人工智慧密不可分的機器學習。課程內容包含了機器學習基礎理論（包含 1990 年代發展的VC理論）、分類器（包含決策樹及支援向量機）、神經網路（包含深度學習）及增強式學習（包含深度增強式學習。
此部份技術包含最早追溯至 1950 年代直到最近 2016 年附近的最新發展。此課程從基礎理論開始，簡介了各機器學習主流技法以及從淺層學習架構演變到最近深度架構的轉換。
本課程之核心目標為：
Read more
本課程第二部分著重在和人工智慧密不可分的機器學習。課程內容包含了機器學習基礎理論（包含 1990 年代發展的VC理論）、分類器（包含決策樹及支援向量機）、神經網路（包含深度學習）及增強式學習（包含深度增強式學習。
此部份技術包含最早追溯至 1950 年代直到最近 2016 年附近的最新發展。此課程從基礎理論開始，簡介了各機器學習主流技法以及從淺層學習架構演變到最近深度架構的轉換。
本課程之核心目標為：
（一）使同學對人工智慧相關的機器學習技術有基礎概念
（二）同學能夠理解機器學習基礎理論、分類器、神經網路、增強式學習
（三）同學能將相關技術應用到自己的問題上
修課前，基礎背景知識：
需要的先備知識：計算機概論
建議的先備知識：資料結構與演算法
Register for this course and see more details by visiting:
OpenCourser.com/course/i4l0pz/Ren
What's inside
Syllabus
Concept learning
Computational Learning Theory
Classification
Read more
Syllabus
Concept learning
Computational Learning Theory
Classification
Read more
Neural Network and Deep learning
Reinforcement learning
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines core ML techniques, including classifiers, neural networks, and reinforcement learning, relevant to AI
Covers topics such as computational learning theory and concept learning, providing a theoretical foundation
Progresses from浅層學習架構 to deep learning, showcasing the evolution of ML
Suitable for learners with a foundation in computer science
Recommended for learners with knowledge in data structures and algorithms
Save this course
Save 人工智慧：機器學習與理論基礎 (Artificial Intelligence - Learning & Theory) to your list so you can find it easily later:
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Reviews summary
Ai foundations: theory and machine learning
This intermediate-level course is an excellent pick to learn the core concepts of machine learning and their application to real-world problems. Reviews indicate that the instructor's explanations are easy to understand even for those without an extensive AI background.
Simple Quizzes
"習題的內容算簡單, 大部份在檢驗觀念"
Suitable for Beginners
"當初沒有注意到這是給有一點基礎的人上的課，因為自己完全沒有基礎但想試試看學習人工智慧的基礎理論，不過老師講得非常好"
Clear Explanations
"Professor Ding's teaching is conscientious and the lectures are clearly explained"
Lack of Responsiveness
"Lecturer explains concepts very clearly and explanations are easy to understand... However, lecturer or TA doesn't reply. Beside, I also try to contact with lecturer via NTU email but still no any response"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in 人工智慧：機器學習與理論基礎 (Artificial Intelligence - Learning & Theory) with these
activities:
Connect with a mentor or expert in the field of machine learning
Show steps
Gain insights from experienced professionals and get personalized advice
Browse courses on
Mentorship
Show steps
-
Attend industry events and meetups
-
Reach out to professors, researchers, or practitioners in the field
Review basics of probability and statistics
Show steps
Reinforce your understanding of the fundamental building blocks of machine learning
Browse courses on
Probability
Show steps
-
Solve problems from textbooks or online resources
-
Revisit your notes and textbooks from a previous probability and statistics course
-
Look up online tutorials and do practice exercises
Participate in online discussion forums and collaborate with peers
Show steps
Connect with other learners, exchange knowledge, and get feedback on your ideas
Browse courses on
Peer Support
Show steps
-
Join online discussion forums and introduce yourself
-
Ask questions, share insights, and engage in discussions
-
Collaborate on projects or assignments
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow guided tutorials on supervised and unsupervised learning
Show steps
Build hands-on experience with different machine learning algorithms and techniques
Browse courses on
Supervised Learning
Show steps
-
Identify a few well-regarded online tutorials or courses
-
Follow the tutorials to implement algorithms and build models
-
Experiment with different parameters and datasets to observe their impact on model performance
Solve practice problems on machine learning algorithms
Show steps
Develop a deeper understanding of the strengths and weaknesses of different machine learning algorithms
Browse courses on
Predictive Modeling
Show steps
-
Find practice problems on websites like LeetCode, HackerRank, or Kaggle
-
Attempt to solve the problems on your own
-
Review solutions and learn from your mistakes
Build a machine learning model for a real-world problem
Show steps
Apply your skills to solve a practical problem and gain valuable hands-on experience
Show steps
-
Define the problem and collect relevant data
-
Choose appropriate machine learning algorithms and train models
-
Evaluate model performance and iterate to improve results
Contribute to open source projects related to machine learning
Show steps
Gain practical experience and contribute to the advancement of the field
Browse courses on
Open Source
Show steps
-
Identify open source projects on platforms like GitHub
-
Find areas where you can contribute your skills
-
Submit code contributions and participate in discussions
Connect with a mentor or expert in the field of machine learning
Show steps
Gain insights from experienced professionals and get personalized advice
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Attend industry events and meetups
- Reach out to professors, researchers, or practitioners in the field
Review basics of probability and statistics
Show steps
Reinforce your understanding of the fundamental building blocks of machine learning
Browse courses on
Probability
Show steps
Show steps
Show steps
- Solve problems from textbooks or online resources
- Revisit your notes and textbooks from a previous probability and statistics course
- Look up online tutorials and do practice exercises
Participate in online discussion forums and collaborate with peers
Show steps
Connect with other learners, exchange knowledge, and get feedback on your ideas
Browse courses on
Peer Support
Show steps
Show steps
Show steps
- Join online discussion forums and introduce yourself
- Ask questions, share insights, and engage in discussions
- Collaborate on projects or assignments
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow guided tutorials on supervised and unsupervised learning
Show steps
Build hands-on experience with different machine learning algorithms and techniques
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Identify a few well-regarded online tutorials or courses
- Follow the tutorials to implement algorithms and build models
- Experiment with different parameters and datasets to observe their impact on model performance
Solve practice problems on machine learning algorithms
Show steps
Develop a deeper understanding of the strengths and weaknesses of different machine learning algorithms
Browse courses on
Predictive Modeling
Show steps
Show steps
Show steps
- Find practice problems on websites like LeetCode, HackerRank, or Kaggle
- Attempt to solve the problems on your own
- Review solutions and learn from your mistakes
Build a machine learning model for a real-world problem
Show steps
Apply your skills to solve a practical problem and gain valuable hands-on experience
Show steps
Show steps
Show steps
- Define the problem and collect relevant data
- Choose appropriate machine learning algorithms and train models
- Evaluate model performance and iterate to improve results
Contribute to open source projects related to machine learning
Show steps
Gain practical experience and contribute to the advancement of the field
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify open source projects on platforms like GitHub
- Find areas where you can contribute your skills
- Submit code contributions and participate in discussions
Career center
Learners who complete 人工智慧：機器學習與理論基礎 (Artificial Intelligence - Learning & Theory) will develop knowledge and skills
that may be useful to these careers:
Artificial Intelligence Architect
Artificial Intelligence Architect
Hold an integral role in the design and development of artificial intelligence systems. As the backbone of AI, your skills in machine learning, such as reinforcement learning, neural networks, and classification, are essential. This course may help you build a solid theoretical foundation and practical knowledge in these areas.
Data Mining Analyst
Data Mining Analyst
As a Data Mining Analyst, you extract raw data, convert it into valuable information to identify patterns, then, in turn, leverage those patterns to make sound business decisions. This course provides a comprehensive introduction to machine learning techniques that are vital for data mining, such as neural networks, classification, and reinforcement learning.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers are experts in deep learning, a subfield of machine learning that has revolutionized AI. This course can help build a solid foundation in neural networks and deep learning, which are crucial technologies for deep learning engineers.
Artificial Intelligence Developer
Artificial Intelligence Developer
As an AI Developer, you are responsible for the design and implementation of intelligent systems. This course provides a comprehensive introduction to machine learning techniques that are used in AI development, including neural networks, reinforcement learning, and classification.
Machine Learning Researcher
Machine Learning Researcher
As a Machine Learning Researcher, you will push the boundaries of machine learning and advance artificial intelligence. This course may prove useful by providing a deep dive into the theoretical foundations of machine learning, including computational learning theory and reinforcement learning.
Computational Linguist
Computational Linguist
Computational Linguists apply computer science techniques to analyze and understand human language. This course may be useful in providing a foundation in machine learning, including neural networks, which are used in computational linguistics to process and analyze natural language.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers bring artificial intelligence to life! Design and build machine learning models that bring automation to a wide range of tasks. This course may provide a useful foundation in machine learning basics such as reinforcement learning, neural networks, classification, and computational learning theory.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers combine computer science and machine learning to build powerful systems that see and interpret the world around us. This course may be useful in providing a strong foundation in deep learning and neural networks, which are crucial technologies in the field of computer vision.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers specialize in enabling computers to understand and generate human language. This course may be useful in establishing a foundation in neural networks and deep learning, which are essential technologies for natural language processing.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, construct, and maintain robots. This course provides a useful introduction to machine learning, including reinforcement learning, which is used in robotics to control and navigate robots effectively.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data. This course may be useful in providing a foundation in reinforcement learning, which is used in quantitative finance to optimize investment strategies.
Data Scientist
Data Scientist
As a Data Scientist, you use advanced techniques and knowledge in the field of large data and use it to extract meaningful information. This course may help you establish a foundation in machine learning theory and techniques, including deep learning, classification, and reinforcement learning. These are fundamental aspects of large data analysis.
Software Engineer
Software Engineer
A Software Engineer will solve business and technical problems through the design and development of computer software. This course helps build a foundation in the theory and application of machine learning, providing valuable knowledge in reinforcement learning and neural networks.
Computer Scientist
Computer Scientist
Computer Scientists conduct research and design innovative solutions across a wide range of computing disciplines. This course may provide a useful foundation in machine learning, including neural networks, which are used in computer science to solve complex computational problems.
Cognitive Scientist
Cognitive Scientist
Cognitive Scientists seek to understand the human mind by studying the brain and behavior. This course can help build a foundation in the theoretical underpinnings of artificial intelligence, providing valuable insights into how the human mind works.
For more career information including salaries, visit:
OpenCourser.com/course/i4l0pz/Ren
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
人工智慧：機器學習與理論基礎 (Artificial Intelligence - Learning & Theory).
這本書是模式識別和機器學習領域的經典教科書，內容涵蓋模式識別和機器學習的基礎理論、演算法和應用，適合作為本課程的補充教材。
這本書是機器學習領域的經典教科書，內容涵蓋機器學習基礎理論、演算法和應用，適合作為本課程的補充教材或延伸閱讀。
這本書是增強式學習領域的經典教科書，內容涵蓋增強式學習的基礎理論、演算法和應用，適合作為本課程的延伸閱讀。
這本書是機器學習領域的入門教科書，內容涵蓋機器學習的基礎理論、演算法和應用，適合作為本課程的補充教材。
這本書是神經網路和深度學習領域的入門教科書，內容涵蓋神經網路和深度學習的基礎理論、演算法和應用，適合作為本課程的補充教材。
Save
這本書是深度學習領域的權威著作，內容涵蓋深度學習的基礎理論、模型和應用，適合作為本課程的延伸閱讀。
這本書是深度學習領域的入門教科書，內容涵蓋深度學習的基礎理論、演算法和應用，以及使用 Python 實作深度學習模型的實作範例，適合作為本課程的補充教材。
這本書是機器學習領域的入門教科書，內容涵蓋機器學習的基礎理論、演算法和應用，以及使用 R 實作機器學習模型的實作範例，適合作為本課程的補充教材。
這本書是統計學習領域的經典教科書，內容涵蓋統計學習的基礎理論、演算法和應用，適合作為本課程的延伸閱讀。
這本書是機器學習領域的進階教科書，內容涵蓋機器學習的基礎理論，以及使用機率論的觀點來探討機器學習問題，適合作為本課程的延伸閱讀。
這本書是計算學習理論領域的權威著作，內容涵蓋計算學習理論的基礎理論和最新進展，適合作為本課程的延伸閱讀。
這本書是人工智慧領域的經典教科書，內容涵蓋人工智慧的基礎理論、演算法和應用，適合作為本課程的延伸閱讀。
For more information about how these books relate to this course, visit:
OpenCourser.com/course/i4l0pz/Ren
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Level
Intermediate
Via
Coursera
Institution
National Taiwan University
Instructor
于天立
Language
中文
Good to know
Examines core ML techniques, including classifiers, neural networks, and reinforcement learning, relevant to AI
Covers topics such as computational learning theory and concept learning, providing a theoretical foundation
Progresses from浅層學習架構 to deep learning, showcasing the evolution of ML
Suitable for learners with a foundation in computer science
Recommended for learners with knowledge in data structures and algorithms
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