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Syllabus

Infomation of Course

Program Master/Doctorate [석/박사과정] Course Type Elective(Graduate) [ 선택(석/박사) ]
Course Code 31.801 Course No IE801
Section B English English
L:L:C(AU) 3:0:3.0(0) Exam time
(classroom)
- Wed: 09:00~11:45
()
Course Title Special Topics in Industrial Engineering II<Data Analysis with Deep Learning> [ 산업공학특수논제Ⅱ<딥러닝 기반 데이터분석> ]
Class time
(classroom)
Mon: 10:30~12:00 / (E2)Industrial Engineering & Management Bldg. [ (E2)산업경영학동 ] (1122)
Wed: 10:30~12:00 / (E2)Industrial Engineering & Management Bldg. [ (E2)산업경영학동 ] (1122)
Notice Code Share KSE801B

Information of Professor

Name 이재길(Lee, Jae-Gil)
Department 산업및시스템공학과(Department of Industrial & Systems Engineering)
Phone 042-350-1617
E-Mail jaegil@kaist.ac.kr

Plan of Lecture

Syllabus File Syllabus (KSE801).pdf
Syllabus URL
Summary of Lecture Deep learning has played an important role in big data analysis in recent years. This new course teaches recent applications of deep learning, such as convolutional neural networks (CNN), recurrent neural networks (RNN), and auto encoders, for various data analysis problems. These selected problems will mostly deal with mobility data and time-series data. The course conforms to KAIST Education 4.0. That is, the proportion of traditional lectures is less than half, and the students should participate in discussion and presentation.
Material for Teaching - Auxiliary textbook: Aurelien Geron, Hands-On Machine Learning with Scikit-Learn and TensorFlow, O'Reilly, 2017.
- Auxiliary textbook: Rezaul Karim, Predictive Analytics with TensorFlow, Packt, 2017.
- Auxiliary textbook: Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, MIT Press, 2016.
Evaluation Criteria - Midterm exam: 30%
- Assignments: 20%
- Presentation: 10%
- Term project: 40% (latency penalty: 20%)
- Class attendance: deducting 1 point after 3 absences
Lecture Schedule 01 Week: Introduction, Deep Learning Review
02 Week: Deep Learning Review, Python Exercise
03 Week: Time Series (e.g., Web Traffic) Prediction
04 Week: Time Series (e.g., Web Traffic) Prediction
05 Week: Time Series Anomaly Detection
06 Week: Time Series Anomaly Detection
07 Week: Term Project Progress Report
08 Week: Midterm Exam
09 Week: Streaming Service Churn Prediction
10 Week: Streaming Service Churn Prediction
11 Week: Taxi Trip Duration (Time) Prediction
12 Week: Taxi Trip Duration (Time) Prediction
13 Week: Taxi Destination Prediction
14 Week: Taxi Destination Prediction
15 Week: Term Project Final Presentation
16 Week: No Final Exam / Paper Writing
Memo Prerequisite:
- Data mining or machine learning related course
- Python programming