CE6143 - Introduction to Data Science (Fall 2023)

Lecture language: English

Meeting time

14:00 - 16:50 Tuesday

Location

A306, Engineering Building 5

Recommended textbooks

"The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
download
"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
download
"Interpretable Machine Learning" by Christoph Molnar
Download

Staff

Instructor: Hung-Hsuan Chen (陳弘軒)
Office hour: after class or by appointment.
TA: Zih-Hao Huang (黃梓豪), Che-Yu Chou (周哲宇)
Office hour: after class or by appointment

Grading

Exercise 1 (group): 5%
Exercise 2 (individual): 15%
Exercise 3 (individual): 15%
Kaggle competition result and short report (group): 25%
One page proposal for final project (group): 5%
Project final presentation and report (group): 30%
Others (individual): 5%

Slides

Please download from new ee-class

Progress (subject to change)

Week	Date	Content	Exercise
1	9/12	1. Overview of the course
2	9/19	1. Introduction to ML 2. KNN 3. k-means	Exercise 1 (due: 10/2 23:59:59)
3	9/26	1. Distance measures 2. Entropy 3. Decision tree
4	10/3	1. Decision tree 2. Matrix derivatives
5	10/10	Holiday
6	10/17	Linear regression and regularization (Lasso, Ridge, Elastic-net)	Exercise 2 (due: ~~10/30~~11/6 23:59:59)
7	10/24	1. Logistic regression and gradient ascent 2. Evaluation metrics for binary classification
8	10/31	1. Invited talk by Dr. Shuen-Huei Guan and Dr. Jing-Kai Lou from KKCompany 2. Evaluation metrics for multi-class and multi-label classification 3. ROC curve vs PR curve
9	11/7	1. Recommender systems 2. Differentiating regularization weight 3. Factorization Machine and Field-aware Factorization Machine	One page proposal for final project (due: 11/13 23:59:59) Exercise 3 (due: 11/20 23:59:59)
10	11/14	1. Learning to rank 2. Linear SVM	Kaggle competition (begins on 11/13 and ends on 12/25) An one-page report to describe your method (due: 12/27 23:59:59)
11	11/21	1. Kernel SVM 2. Regularized linear regression and classification
12	11/28	1. Linear SVM with poly-2 terms vs. polynomial kernel SVM 2. Practical concerns 3. Invited talk: Explanable AI by Prof. Hao-Tsung Yang
13	12/5	1. Multi-layer perceptron 2. Convolutional neural network
14	12/12	1. Convolutional neural network 2. Recurrent neural network
15	12/19	Associated Learning
16	12/26	Transformer and Large Language Model
17	1/2	Flexible learning week: (1) Recorded invited talk by Prof. Chin-Te Lin (2) Field trip to the FESTO lab	Final project due: 1/2 23:59:59
18	1/9	Flexible learning week: Explainable AI and ethics