2016 Fall Undergraduate Course in
Computer Science and Engineering
Instructor:
Prof. Byoung-Tak Zhang
TA:
Sang-Woo Lee (Room: 138-417, Tel.: 02-880-5890), Hanock Kwak (Room: 302-314-1, Tel.: 02-880-1847)
Classroom:
302-107
Time:
Tue & Thu, 11:00-12:15
Reference:
Evaluation:
- Two open-book exams (50%)
- Two miniprojects + two reports + one poster presentation (40%)
- Participation and discussion (10%)
Announcement:
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Course Description:
- Machine learning studies architectures and algorithms for automatically constructing models (or computer programs) from observed data.
Machine learning systems design software not by manual programming but by automatic programming through feeding data, and thus can improve their performance on their own by observing more data.
Deep learning is a class of machine learning models that employs many layers of neural network structures and, recently, has revolutionized the AI industry by solving many difficult problems, such as image recognition, speech recognition, natural language processing, self-driving cars, and autonomous robots.
This course gives an introduction to machine learning and neural networks with an emphasis on deep learning models.
We study the basic mathematical algorithms of various deep learning models and their applications in vision, speech, language, robotics, games, and digital media.
Course attendants are expected to do their own deep learning AI project on a problem chosen by the participants.
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-
Week |
Topics |
Slides |
Week 1 (9/1)
|
- Artificial Intelligence and Machine Learning
|
|
Week 2
(9/6, 9/8)
|
- Neural Networks and Delta Learning
|
01
02
03
04
|
Week 3
(9/13)
|
- Multilayer Neural Networks and Backpropagation Learning
|
05
06
07
08
|
Week 4
(9/20, 9/22) |
- Deep Learning: What, Why, and How?
|
09
10
|
Week 5
(9/27, 9/29) |
|
Practice 01 (Python)
Practice 02 (Tensorflow) |
Week 6
(10/4, 10/6) |
- Convolutional Neural Networks (CNN)
|
11
12
|
Week 7
(10/11, 10/13) |
|
Using Neural Network
Practice 03 (Tensorflow)
[code]
|
Week 8
(10/18, 10/20) |
|
|
Week 9
(10/25, 10/27) |
- Deep Belief Networks (DBN)
|
|
Week 10
(11/1, 11/3) |
- Deep Hypernetworks
- Applications of DBN
|
(10/31)
(11/1) DHN for KidsVideo
(11/3) Practice 04 (Tensorflow,DBN)
|
Week 11
(11/8, 11/10) |
- Recurrent Neural Networks (RNN)
|
Practice 05 (Tensorflow,Seq2Seq)
|
Week 12
(11/15, 11/17) |
- Recurrent Neural Networks (RNN)
|
13
14
|
Week 13
(11/22, 11/24) |
|
Google's NMT
|
Week 14
(11/29, 12/1) |
- Recent Advancements in Deep Learning
|
15
16
|
Week 15
(12/6, 12/8) |
|
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Week 16
(12/13) |
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