Build data-driven AI solutions using Python, statistics, and machine learning techniques.
This course introduces students to the Python programming language essential for data manipulation, statistical analysis, and predictive modeling techniques required for machine learning and artificial intelligence.
We will explore the wonderfully concise and expressive use of Python’s advanced module features and apply it in probability, statistical analysis, training models, and various other applications. Students will explore mathematical operations with array data structures, optimization, probability density function, interpolation, visualization, and other high-performance benefits of core scientific packages such as NumPy, Pandas, scikit-learn, and Matplotlib.
Additionally, students will learn modern machine learning concepts and techniques, including supervised, unsupervised, and semi-supervised learning, to develop predictive models using Python libraries. The course concludes with a real-world, end-to-end machine learning project, providing students with practical experience in solving challenging problems.
Basic Programming Knowledge as can be acquired in Python Programming for Beginners (CMPR.X415) and a knowledge of Fundamentals of Statistics
Students may still enroll if they missed the 1st class session. However, they need to communicate with the instructor via Canvas and catch up on all missed work prior to the 2nd class meeting.
This class meets simultaneously in a classroom and remotely via Zoom. Students are expected to attend and participate in the course, either in-person or remotely, during the days and times that are specified on the course schedule. Students attending remotely are also strongly encouraged to have their cameras on to get the most out of the remote learning experience. Students attending the class in-person are expected to bring a laptop to each class meeting.
To see all meeting dates, click "Full Schedule" below.
Electronic Course Materials: You will be granted access in Canvas to your course site and course materials approximately 24 hours prior to the published start date of the course.
Required Text: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow; Aurelien Geron; O'Reilly Media Inc.; 2022. ISBN: 9781098122461
Recommended Text: Python Data Science Handbook; Jake VanderPlas; O'Reilly Media Inc.; 2023. ISBN: 9781098121228
Machine Learning with Python Cookbook; Gallatin and Albon; O'Reilly Media Inc.; 2023. ISBN: 9781098135690
Introduction to Machine Learning with Python; Muller and Guido; O'Reilly Media Inc.; 2023. ISBN: 9781449369897
This class meets simultaneously in a classroom and remotely via Zoom. Students are expected to attend and participate in the course, either in-person or remotely, during the days and times that are specified on the course schedule. Students attending remotely are also strongly encouraged to have their cameras on to get the most out of the remote learning experience. Students attending the class in-person are expected to bring a laptop to each class meeting.
To see all meeting dates, click “Full Schedule” below.
Electronic Course Materials: You will be granted access in Canvas to your course site and course materials approximately 24 hours prior to the published start date of the course.
Required Text: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow; Aurelien Geron; O'Reilly Media Inc.; 2022. ISBN: 9781098122461
Recommended Text: Python Data Science Handbook; Jake VanderPlas; O'Reilly Media Inc.; 2023. ISBN: 9781098121228
Machine Learning with Python Cookbook; Gallatin and Albon; O'Reilly Media Inc.; 2023. ISBN: 9781098135690
Introduction to Machine Learning with Python; Muller and Guido; O'Reilly Media Inc.; 2023. ISBN: 9781449369897
This class meets simultaneously in a classroom and remotely via Zoom. Students are expected to attend and participate in the course, either in-person or remotely, during the days and times that are specified on the course schedule. Students attending remotely are also strongly encouraged to have their cameras on to get the most out of the remote learning experience. Students attending the class in-person are expected to bring a laptop to each class meeting.
To see all meeting dates, click "Full Schedule" below.
You will be granted access in Canvas to your course site and course materials approximately 24 hours prior to the published start date of the course.
Required Textbook: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow; Aurelien Geron; O'Reilly Media Inc.; 2022. ISBN: 9781098122461
Recommended Textbooks:
Python Data Science Handbook; Jake VanderPlas; O'Reilly Media Inc.; 2023. ISBN: 9781098121228
Machine Learning with Python Cookbook; Gallatin and Albon; O'Reilly Media Inc.; 2023. ISBN: 9781098135690
Introduction to Machine Learning with Python; Muller and Guido; O'Reilly Media Inc.; 2023. ISBN: 9781449369897
