In computational science, curve fitting falls into the category of non-linear approximation. Unlilke what we have discussed in Class 1. Regression, the functional shape now has one of the two following shapes.

Many people think machine learning is some sort of magic wand. If a scholar claims that a task is mathematically impossible, people start asking if machine learning can make an alternative. The truth is, as discussed from all pre-requisite courses, machine learning is nothing more than a computer version of statistics, which is a discipline that heavily relies on mathematics.

• [COM503] Deep Learning — Swiss Institute of Artificial Intelligence (siai.org)

Model examination is available from above link. The last class solves previous year's (or similar) exam while covering key components of the earlier classes.

Generative models are simply repeatedly updated model. Unlike discriminative models that we have learned from all previous lectures, such as linear/non-linear regressions, SVM, tree models, and Neural Networks, Generative models are closely related to Bayesian type updates. RBM (Restricted Boltzmann Machine) is one of the example models that we learned in this class. RNN, depending on the weight assignment for memory, can qualify generativeness.

Recurrent Neural Network (RNN) is a neural network model that uses repeated processes with certain conditions. The conditions are often termed as 'memory', and depending on the validity and reliance of the memory, there can be infinitely different variations of RNN. However, whatever the underlying data structure it can fit, the RNN model is simply an non-linear & multivariable extension of Kalman filter.

As shown by RBM's autoencoder versions, if the neural network is well-designed, it can perform better than PCA in general when it comes to finding hidden factors. This is where image recognition relies on neural network.

Constructing an Autoencoder model looks like an art, if not computationally heavy work. A lot of non-trained data engineers rely on coding libraries and a graphics card (that supports 'AI' computation), and hoping the computer to find an ideal Neural Network. As discussed in previous section, the process is highly exposed to overfitting, local maxima, and humongous computational cost. There must be more elegant, more reasonable, and more scientific way to do so.

Bayesian estimation tactics can be used to replace arbitrary construction of deep learning model's hidden layer. In one way, it is to replicate Factor Analysis in every layer construction, but now that one layer's value change affects the other layers. This process goes from one layer to all layers. What makes this job more demanding is that we are still unsure the next stage's number of nodes (or hidden factors) are right, precisely as we are unsure about the feeding layer's node numbers. In fact, everything here is unsure, and reliant to each other.

As was discussed in [COM502] Machine Learning, the introduction to deep learning begins with history of computational methods as early as 1943 where the concept of Neural Network first emerged. From the departure of regression to graph models, major building blocks of neural network, such as perceptron, XOR problem, multi-layering, SVM, and pretraining, are briefly discussed.

Feed forward and back propagation have significant advantage in terms of speed of calculation and error correction, but it does not mean that we can eliminate the errors. In fact the error enlarges if the fed data leads the model to out of convergence path. The more layers there are, the more computational resources required, and the more prone to error mis-correction due to the structure of serial correction stages in every layer.

본 문서는 GIAI 산하에서 운영되는 스위스AI대학(Swiss Institute of Artificial Intelligence, SIAI)의 강의노트 중 일부를 한국어로 번역한 것입니다. 영어 원문 및 전체 버전은 아래의 링크를 통해 확인하시기 바랍니다.

• 학부/예비석사 과정
• 정규 석사 과정

아래에 번역된 노트는 학부/예비석사 과정, 혹은 AI MBA 과정에서 발췌 했습니다. 학업에 바쁜 와중에도 번역을 맡아주신 김광재(MBA AI/BigData, 2023), 전웅(MBA AI/BigData, 2023) 학생들께 감사를 표합니다.

데이터 사이언스에서의 수학은 엄밀한 수학이 아니라 긴 문장을 짧게 표현한 것에 불과해데이터 사이언스는 수식이 의미하는 바를 직관적으로 이해하는 자세 필요해경제학에서 수학 기반 연구가 주류로 자리 잡은 이유는 수학이 효율적인 의사소통 수단이기 때문

고등학교 때 수학이 가장 자신 있는 과목이자 가장 좋아하는 과목이었다. 당연히 대학교에 진학해도 수학을 좋아할 줄 알았지만, 대학 시절부터 수학은 싫어하는 과목으로 바뀌었다. 수학 성적이 박사 입학에 중요한 요소였기 때문에 수학 수업을 들었지만, 수년간 대학원에서 공부한 후에도 여전히 수학을 좋아하지 않는다(많은 사람들이 믿지 않지만). 단지 수수께끼를 풀 때 사용하는 수학을 좋아했다는 사실을 깨달았다.

People following AI hype are mostly completely misinformedAI/Data Science is still limited to statistical methodsHype can only attract ignorance

As a professor of AI/Data Science, I from time to time receive emails from a bunch of hyped followers claiming what they call 'recent AI' can solve things that I have been pessimistic. They usually think 'recent AI' is close to 'Artificial General Intelligence', which means the program learns by itself and it is beyond human intelligence level.

Math in AI/Data Science is not really math, but a shortened version of English paragraph.

Korean GDP growth was 6.4%/y for 50 years until 2022, but down to 2.1%/y in 2020s.Due to low birthrate down to 0.7, population is expected to 1/2 in 30 years.Policy fails due to nationwide preference to leftwing agenda.