Women’s AI Study

(1) Outline of the course

This is a training course for beginners (total of 10 sessions) for those who want to study or are interested in learning about AI, limited to girls.

In the first half of the course, participants will learn basic knowledge about AI and the fundamentals of machine learning through group work and quizzes. The content will be made more practical by incorporating the latest application examples and coding exercises. There will also be an opportunity to hear interesting stories from companies about real-life examples of how they work.

In the second half of the session, we will have a group competition for ideas on AI. We will also receive FB from professors, which will lead to further growth.

Another feature of this workshop is to create horizontal connections among female students who share the same interests.

(2) Contents of each session

Session 1 Lecture 1: “AI, Machine Learning, and Reinforcement Learning” 10/2

In the first lecture, we will review the history of AI in order to understand its overall picture. The lecture will then focus on the classification of machine learning, which plays a core role, and explain supervised, unsupervised, and reinforcement learning. For reinforcement learning in particular, specific examples of its use will also be presented. The lecture will also touch on Python, the foundation of AI programming language.

Session 2: Lecture 2 “Supervised and Unsupervised” 10/9

This lecture will focus on the difference between supervised and unsupervised learning methods, a classification of machine learning, and explain the advantages and disadvantages of each. Supervised learning is an approach that uses labeled training data to train models, while unsupervised learning has no labels. Examples of applications and specific learning methods (regression analysis, decision trees, k-means method, etc.) will then be introduced. From this session, students will actually work hands-on and engage in coding exercises while using the book.

Session 3: Lecture 3 “Deep Learning” 10/16

This lecture explains deep learning, a type of machine learning, and its application examples. Deep learning can process complex features by using multi-layer neural networks. It has attracted much attention in recent years for its applications in image recognition, speech recognition, and natural language processing. The course also explains the challenges that AI poses, allowing students to acquire literacy. Finally, students will engage in coding exercises.

Session 4: Lecture 4 “AI Solution and Development” 10/23

The structure of an AI solution will be explained along with its flow. It includes a series of steps: data collection, data preprocessing, partitioning into training and test data, selecting an appropriate model, training the model, evaluating the model, improving the model, and deployment. Finally, students will engage in coding exercises.

Session 5 10/30

Guest Speaker (1)

Content to be determined

Session 6 Lecture 5: “Examples of AI Business Applications and Latest Research” 11/6

This lecture will cover examples of AI implementation in various industries to deepen understanding of how AI is solving problems in the real world. In addition, the course will also cover AI that is currently in the research stage to provide students with cutting-edge examples. Finally, students will engage in coding exercises.

Session 7, November 13

Guest Speaker (2)

Content TBD

An overview of the idea-thon will be given and the participants will be divided into groups. The contents discussed will be presented in the final session. Students will be asked to think about specific industries and learning methods to apply what they have learned in this lecture.

Session 8: December 4

Students will be divided into groups to discuss the Idea-thon.

Session 9: 12/11

Students will be divided into groups to discuss the idea-thon.

Session 10: 12/18

Presentation of the idea-thon