This course introduces the concepts, techniques, and applications of data mining and artificial intelligence (AI), and it mainly focuses on some methods and models that are useful in analyzing and mining real-world data. It will cover frequent regression classification, clustering, and classic deep learning framework. This course is designed to provide a multifaceted learning experience through various instructional formats: lectures, case studies, seminars, and group projects. The course includes theoretical foundations and practical exercises, preparing students for advanced studies or AI and data mining careers.
Prerequisite(s): Grade of C or higher in COS 285 or permission of instructor.
Learning Outcomes
By the end of this course, students will be able to:
- Identify problems where artificial intelligence techniques are applicable.
- Apply selected basic AI techniques; judge the applicability of more advanced techniques.
- Participate in the design of systems that act intelligently and learn from experience.
Textbook (not required)
Data Mining: Concepts and Techniques (3rd Edition, 2012), Jiawei Han, Micheline Kamber, Jian.
Introduction to Data Mining (2nd Edition, 2019), Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar.
Python for Data Analysis (3rd Edition),(Open Edition), Wes McKinney, published by O’Reilly Media.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (3rd Edition, 2022) Aurélien Géron.
Dive into Deep Learning, Aston Zhang, Alexander J. Smola, Zachary Lipton, Mu Li.
Syllabus
Offered
Fall even years