SemesterFall Semester, 2023
DepartmentSophomore Class of BA in Global Governance
Course NameData Science
InstructorLU HSIN-TSE
Credit3.0
Course TypeSelectively
Prerequisite
Course Objective
Course Description
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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

Self-made teaching materials

Lecture

2

What is the problem on Data

Self-made teaching materials

  • Lecture

  • Homework: Collecting a tiny dataset.

3

CV - Introduction to Image Segmentation

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: clustering (K-Means, KNN) segmentation

  • Homework: semantic segmentation.

4

CV - Introduction to Image Classification

  • Lecture

  • Practice: SVM classification

  • Homework: Parameter tuning.

5

National Day (Oct-10th)

6

CV - Image Generation

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)

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)

  • Lecture

  • Practice: Text processing

  • Homework: Semantic analysis

9

NLP - Introduction to Text Generation

  • Lecture

  • Practice: Markov Chain, GPT-2

  • Homework: Compare Markov Chain with ChatGPT

10

NLP – Other tasks (1)

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)

  • Lecture

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

  • Homework: question answering

12

Project Proposal

 

Presentation

13

Time Series - Introduction to Time Series Forecasting

Self-made teaching materials

  • Lecture

  • Practice: ARIMA model

14

Time Series - Introduction to Time Series Forecasting

Self-made teaching materials

  • Lecture

  • Practice: RNN

  • Homework: stock price prediction, performance comparision

15

In-class speech

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

16

Final Project

 

 

17

Final Project

 

 

18

Final Project

 

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 policies, we would switch to online classes (Google Meets) if the COVID-19 pandemic outbreak occurs again.

  2. Homework (40%): 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.

  3. Final Project (50%): 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


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