SemesterFall Semester, 2025
DepartmentSophomore Class of BA in Global Governance
Course NameData Science
InstructorLU HSIN-TSE
Credit3.0
Course TypeSelectively
Prerequisite
Course Objective
Course Description
Course Schedule




































































































週次

課程主題

課程內容與指定閱讀

教學活動與作業

1

Introduction to data & ChatGPT

(09/02)

Self-made teaching materials

Lecture

2

CV - Introduction to Image Labeling

(09/09)

Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009, June). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition (pp. 248-255). Ieee.

  • Lecture

  • Practice: Data collection

  • Homework: Collect images from Google Colab 

3

What is the problem on Data

(09/16)

  • Lecture

  • Practice: clustering (K-Means, KNN) segmentation

  • Homework: semantic segmentation.

4

CV - Introduction to Image Classification

(09/23)

  • Lecture

  • Practice: SVM classification

  • Homework: Metrics.
5 CV - Introduction to Image Segmentation

(09/30)

6

CV - Image Generation

(10/07)

Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10684-10695).

  • Lecture

  • Practice: Stable diffusion & Midjourney

  • Homework: Training a classification model by using AI generated content, & performance comparison

7

NLP - Introduction to Language Modeling (1)

(10/14)

Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9.

  • Lecture

  • Practice: Word Vector & classification

8

NLP - Introduction to Language Modeling (2)

(10/21)



Midterm: 3-5 calculation questions

  • Lecture

  • Practice: Text processing

  • Homework: Semantic analysis

9

NLP - Introduction to Text Generation

(10/28)

  • Lecture

  • Practice: Markov Chain, GPT-2

  • Homework: Compare Markov Chain with ChatGPT

10

NLP – Other tasks (1)

(11/04)

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.

  • Lecture

  • Practice: Machine reading comprehension & sentence classification by using HuggingFace

  • Homework: sentence classification

11

NLP – Other tasks (2)

(11/11)

  • Lecture

  • Practice: Machine reading comprehension & question-answering by using HuggingFace

  • Homework: question answering

12

NLP Workshop

(11/18)

 

  • Flexible Week

  • TA lead CSCL workshop about Classification

13

NLP Workshop

(11/25)

 

  • Lecturer lead CSCL workshop about Evaluation

14

NLP - Pretraining Model (12/02)

Self-made teaching materials

  • Lecture

  • Practice: RNN

  • Homework: stock price prediction, performance comparision

15

In-class speech

(12/09)

(Tentative) Mr. Veeresh Ittangihal, Data Scientist in Micron Technology - Data Scientist in Semiconductor Industry

16

Final Project

(12/16)

 

Final Project Presentation

Teaching Methods
Teaching Assistant
Requirement/Grading

  1. Attendance (10%): This course will be an in-person class, you have to come to the classroom every week. Based on school epidemic prevention policie.

  2. Midterm (30%): Covers 3~5 calculation questions on data cleaning, transformation, and basic analysis. Worth 30% of the final grade.

  3. Homework (30%): Homework will be assigned almost every week, and you (or your team) should submit it to the learning management system (Google Classroom) on time. This course won't accept any delayed submissions.

  4. Final Project (30%): We are going to publish one data set on the Internet, and you (or your team) have to collect data, describe data characteristics, explain tasks, and give a simple demo on your data in order to prove the data quality.



 


Textbook & Reference

  1. Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009, June). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition (pp. 248-255). Ieee.

  2. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.

  3. Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... & Sutskever, I. (2021, July). Learning transferable visual models from natural language supervision. In International conference on machine learning (pp. 8748-8763). PMLR.

  4. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9.

  5. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10684-10695).

  6. Self-made teaching materials


Urls about Course
Attachment