Digital Signal Processing | Coursera

1.2.a Discrete-time signals Discrete-time signals Discrete-time signal:= A sequence of complex numbers Dimension = 1 (for now) Notation: where is an integer Two-sided sequences: is one-dimensional “time”. Analysis: Periodic measurement approach Discrete-time signals can be created by an analysis process where we take periodic measurements of a physical phenomenon. Synthesis: Stream of generated samples Delta […]

Studying Variational Autoencoders

References Arxiv Insights (2018. 2. 25.) Variational Autoencoders. YouTube. [YouTube] Diederik P. Kingma, and Max Welling (2014). Auto-Encoding Variational Bayes. ICLR 2014. [paper] Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., … & Lerchner, A. (2017). beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. ICLR 2017. [paper] Higgins, I., […]

Computational Neuroscience | Coursera

Brief information Instructors: Rajesh P. N. Rao, Adrienne Fairhall About this course: This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered […]

Studying Number Sense

Number Sense | Wikipedia Number sense can refer to “an intuitive understanding of numbers, their magnitude, relationships, and how they are affected by operations”. Psychologists believe that the number sense in humans can be differentiated into the approximate number system and the parallel individuation system. The approximate number system is a system that supports the […]

Dynamics and Cognitive Models | MS in CogSci

Lecture 1 | Introduction “Freud was inspired by the theory of thermodynamics and used the term psychodynamics to describe the processes of the mind as flows of psychological energy (libido or psi) in an organically complex brain.” [Psychodynamics – Wikipedia] Lecture 2 | Linear models What is a linear model? If the derivative of a […]

Seminar in Methodology on Experimental Psychology (Fundamentals and Applications of Cognitive Modeling) | MS in CogSci

Brief Information Name (en) : Seminar in Methodology on Experimental Psychology (Fundamentals and Applications of Cognitive Modeling) Name (ko) : 실험심리방법론세미나 (인지모델링의 기초와 응용) Lecturer : Koh, Sungryong 고성룡 Semester : 2018 Fall Major : MS, Cognitive Science Textbook Busemeyer, J. R., & Diederich, A. (2010). Cognitive modeling. Sage. Syllabus : 2018-2_Seminar-in-Methodology-on-Experimental-Psychology.pdf In short To learn cognitive modeling and its […]

Survey on knowledge graph embedding

Papers Q. Wang, Z. Mao, B. Wang and L. Guo, “Knowledge Graph Embedding: A Survey of Approaches and Applications,” in IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 12, pp. 2724-2743, 1 Dec. 2017. Maximilian Nickel, Kevin Murphy, Volker Tresp, Evgeniy Gabrilovich. A Review of Relational Machine Learning for Knowledge Graphs. Proc. IEEE, […]

[YouTube] Demis Hassabis, CEO, DeepMind Technologies – The Theory of Everything

Worth to studying Physics Neuroscience “What I cannot build, I don not understand.” – Richard Feynman Theme Park: one of the games Demis made Demis’ interest areas in the Ph.D course: imagination and memory DeepMind was founded in 2018. is an Apollo prgramme for AI (>100 scientist from machine learning fields and neuroscience fields) Neuroscience-inspired […]

Sequence Modeling | Deep Learning Specialization | Coursera

Course planning Week 1: Recurrent neural networks Learn about recurrent neural networks. This type of model has been proven to perform extremely well on temporal data. It has several variants including LSTMs, GRUs and Bidirectional RNNs, which you are going to learn about in this section. Lectures: Recurrent neural networks C4W1L01 Why sequence models C4W1L02 […]

Convolutional Neural Networks | Deep Learning Specialization | Coursera

Course Planning Week 1: Foundations of convolutional neural networks Learn to implement the foundational layers of CNNs (pooling, convolutions) and to stack them properly in a deep network to solve multi-class image classification problems. Convolutional neural networks C4W1L01 Computer vision C4W1L02 Edge detection example C4W1L03 More edge detection C4W1L04 Padding C4W1L05 Strided convolutions C4W1L06 Convolutions over […]