For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| SUP5001 | Deep Neural Networks: Theory and Applications | 3 | 6 | Major | Master/Doctor | 1-4 | Superintelligence Engineering | - | No |
| This course covers deep learning based on artificial neural network which is advanced on various industrial. This course, especially, give students basic understanding of modern neural networks and their applications in computer vision and natural language understanding. Students learn about Convuloutional networks, RNNs, LSTM, Dropout and more. This course introduceds the major technology trens driving Deep Learning. | |||||||||
| SUP5002 | Computer Architecture and Its Applications to Artificial Intelligence | 3 | 6 | Major | Master/Doctor | 1-4 | Superintelligence Engineering | - | No |
| This course focuses on principles and mechanisms related to the modern computer architecture including numerical representation, arithmetic operations, datapath and pipelining, cache hierarchies, memory systems, storage and I/O systems. As an application of computer architecture, this course also covers recent HW architectures and techniques for efficient training and inference of AI (especially deep learning) models. | |||||||||
| SUP5003 | Machine Learning Algorithms and Applications | 3 | 6 | Major | Master/Doctor | 1-4 | Superintelligence Engineering | English | Yes |
| This course introduces various machine learning models for supervised & unsupervised learning, in order to motivate students who are new to machine learning. In addition, it provides assignments for hands-on practice using scikit-learn (machine learning library operating on python), so that students have chance to apply machine learning models to particular problems with real-world datasets. Regarding supervised learning, we begin with linear regression and introduce ridge regression where L2 regularization method is applied to alleviate over-fitting. Logistic regression is introduced, which is a popular tool for binary classification. k-nearest classifier and Naive Bayes classifier with its application to text classification are also introduced. Tree-based models such as CART is introduced, ensemble of trees, including random forest and boosting are covered. Finally support vector machines, which are large-margin classifiers, are introduced to complete the supervised learning topic. Regarding unsupervised learning, we introduce two clustering methods, including k-means clustering and mixture of Gaussians. For dimensionality reduction, we provide principal component analysis (PCA), nonnegative matrix factorization (NMF), and stochastic neighborhood embedding (SNE). | |||||||||
| SUP5004 | Neural Interface and Application | 3 | 6 | Major | Master/Doctor | 1-4 | Superintelligence Engineering | Korean | Yes |
| The course covers the material, geometry, and other necessary property of neural interface that are used in diagnosis and clinical applications. For example, one topic is to discuss and compare various way of stimulation such as electrical, optical, pharmacological, at both central and peripheral nerve system. The one goal of the course is to develop the ability of solving issues via various engineering aspects. | |||||||||
| SUP5005 | Cloud Systems For Machine Learning | 3 | 6 | Major | Master/Doctor | 1-4 | Superintelligence Engineering | - | No |
| Cloud systems are being used as a core infrastructure for training and inferencing neural networks, which require a large amount of computing resources. This course deals with the core elements of distributed processing and cloud system technology for artificial intelligence and their recent technology trends. Specifically, students will learn the cloud system interface, orchestration technology, and virtualization technology, and learn the principles of distributed learning, the design of distributed learning frameworks, and the design and implementation of a distributed learning cluster in a cloud system. | |||||||||
| SUP5006 | Mathematics for Machine Learning | 3 | 6 | Major | Master/Doctor | 1-4 | Superintelligence Engineering | Korean | Yes |
| This course introduces a few mathematics which is essential for machine learning, to help students to better understand various machine learning models. Regarding linear algebra, we begin with vectors and matrices and cover linear algebraic equations, inner product, vector norms, orthogonal projection. We also introduce a popular matrix decomposition, like SVD and explain how such method is used in machine learning. Regarding probability and distributions, we introduce random variables, expected values, and two exemplar distributions, including Gaussian for continuous random variables and Bernoulli for bianry random variables. Regarding parameter estimation, we introduce maxinum likelihood and MAP estimation methods. Regarding information theory, we introduce entropy, mutual information, KL-divergence, and explain how these are used in machine learning. Regarding continuous optimization, we begin with vector calculus, and introduce two iterative methods such as gradient descdent/ascent, Newton’s method. Finally we conclude this course, having a look at a few machine learning models to understand how relevant mathematics are utilized in machine learning. | |||||||||