Course ID: | CSCI 8945. 4 hours. |
Course Title: | Advanced Representation Learning |
Course Description: | Advanced representation learning algorithms in machine
learning, from the traditional subspace learning models to the
recent deep representation learning models. Applications in the
fields of computer vision, data mining, and natural language
processing will be covered. |
Oasis Title: | Advanced Representation Learn |
Prerequisite: | (CSCI 4380/6380 and CSCI(PHIL) 4550/6550) or permission of department |
Semester Course Offered: | Not offered on a regular basis. |
Grading System: | A-F (Traditional) |
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Course Objectives: | The purpose of this course is to familiarize students with
advanced topics in representation learning, from the
traditional subspace learning models to the recent deep
representation learning models. The course will also cover a
brief review of linear algebra, convex optimization, and
machine learning. Real-world applications in the fields of
computer vision, data mining, and natural language processing
will be introduced as well. The course is appropriate for
graduate students who want to do research in machine learning,
or explore representation learning algorithms for their
research. |
Topical Outline: | 1. Review of linear algebra and convex optimization
2. Machine learning overview
3. Subspace learning
3.1 Linear subspace learning
3.2 Nonlinear subspace learning
3.3 Multi-view subspace learning
3.4 Transfer subspace learning
3.5 Applications in computer vision
4. Deep representation learning
4.1 Word embedding, sentence embedding, and document
embedding
4.2 Adversarial training and virtual adversarial training
4.3 Deep clustering
4.4 Interpretable representation learning
4.5 Applications in natural language processing and
recommender system
5. Graph Learning
5.1 Graph construction
5.2 Graph convolutional networks
5.3 Applications in data mining and computer vision |