TÜRKÇE
ENGLISH
Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| ELECTRICAL-ELECTRONICS ENGINEERING (MASTER) (WITH THESIS) (ENGLISH) | |
| Code | EE002 |
| Name | Statistical Learning Methods and Pattern Recognition |
| Term | 2018-2019 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Belirsiz |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Doç. Dr. TURGAY İBRİKÇİ |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The objective of the Advances Topic in Neural Networks course is to expand on the material covered in the introduction to Neural Networks course (EE589). It focuses on special topics in NN such as exact and approximate inference in graphical models, dimensionality reduction and component analysis methods, latent variable models, models of documents and words, time series models and selected topic from deep neural networks. The course will consist of (each student) presentations and discussions. Students will be evaluated based on their participation in discussions, presentations, and projects that will be a manuscript or a conference paper.
Course Content
Discussion of the results of research, Parameter Optimization Algorithms, Learning and Generalization, Bayesian Techniques, Mixture Models, and Applications of Neural Networks.
Course Precondition
Resources
Notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | The theoretical knowledge |
| LO02 | The basic intuitions needed to use |
| LO03 | Develop effective machine learning solutions to challenging problems. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Statistical Learning Setting | Study related topics | |
| 2 | Regularized Least Squares | Study related topics | |
| 3 | Features and Kernels | Study related topics | |
| 4 | Statistical Learning I | Study related topics | |
| 5 | Statistical Learning II | Study related topics | |
| 6 | Local Methods | Study related topics | |
| 7 | Privacy and Information-Theoretic Stability | Study related topics | |
| 8 | Mid-Term Exam | Study the topics until this week | |
| 9 | Deep Learning Theory: Approximation | Study related topics | |
| 10 | Deep Learning Theory: Optimization | Study related topics | |
| 11 | Deep Learning Theory: Generalization | Study related topics | |
| 12 | Seminars and Presentations | Preparing presentations | |
| 13 | Seminars and Presentations | Preparing presentations | |
| 14 | Seminars and Presentations | Preparing presentations | |
| 15 | Seminars and Presentations | Preparing presentations | |
| 16 | Term Exams | Study all topics | |
| 17 | Term Exams | Study all topics |