2026-1 EEE2113-01
ECE 101
Electrical & Computer Engineering 101
Part-1
AI, Machine Learning , Deep Learning,
Reinforcement Learning,
Generative AI, Agentic AI,
Federated Learning, Transfer Learning, Digital Twin
Jong-Moon Chung, Ph.D., IEEE Fellow, NAEK
Professor, School of Electrical & Electronic Engineering
Professor, Department of Emergency Medicine, College of Medicine
Associate Dean, College of Engineering, Yonsei University
Vice President of IEEE PSES, Director of IEEE CTSoc
2026-1 EEE2113-01 ECE101 Syllabus
2026-1 EEE2113-01 ECE101 Syllabus
2026-1 EEE2113-01 ECE101 Syllabus
Jong-Moon Chung, Emerging Secure
Networks, Blockchains & Smart
Contract Technologies. Springer Nature,
Springer, ISBN 9783031658655, ©2024.
https://link.springer.com/book/10.1007/978
-1-4842-8928-0
Jong-Moon Chung, Emerging Metaverse
XR and Video Multimedia Technologies.
Springer Nature, Apress, ISBN
9781484289273, ©2023.
https://link.springer.com/book/10.1007/978
-3-031-65866-2
2026-1 EEE2113-01 ECE101 Syllabus
2026-1 EEE2113-01 ECE101 Syllabus
2026-1 EEE2113-01 ECE101 Syllabus
정종문 교수/부학장, IEEE Fellow, 한국공학한림원 (NAEK) 정회원
경력(요약)
• 현 연세대학교 공과대학 (국제)부학장
• 현 연세대학교 공과대학 전기전자공학부 교수
• 현 연세대학교 의과대학 응급의학교실 (겸직)교수
• 현 연세대학교 치과대학 치주과학교실 (겸직)교수
• 현 연세대학교 국방융합공학과 학과장
• 현 Director, IEEE Consumer Technology Society (CTSoc)
• 현 부회장(VP), IEEE Product Safety Engineering Society (PSES)
• 현 한국인터넷정보학회 (KSII) 부회장
• 현 국방부 방위사업청 인공지능로봇팀 전문위원
• 현 국방부 방위사업청 첨단기술사업단 및 무인사업부 전문위원
• 현 Section Editor, Wiley ETRI Journal
• 현 Chair EiC (Editor-in-Chief), KSII TIIS Journal
• 2022-2025 Senior Editor, IEEE Transactions on Consumer Electronics
• 2011-2021 Editor, IEEE Transactions on Vehicular Technology
• 2019 대한민국 육군 사이버 자문위원
• 2019 경찰청 연구개발기획단 스마트치안 분과장
• 2017-2018 행안부 안전한국훈련 부단장
• 2011-2012 삼성전자 자문위원
• 2000-2006 Associate Professor (Tenured), Oklahoma State University,
School of Electrical & Computer Engineering, USA
• 1997-2005 PI (Principal Investigator) of US DoD Projects for DARPA,
AFRL, ONR, and NRL
• 2000-2005 Director, Oklahoma Comm., Netw., Bioengineering Lab.
• 1997-1999 Assistant Professor & Instructor, Pennsylvania State
University, Department of Electrical Engineering
수상실적
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2026 연세대학교 우수업적 교수상 연구부문 최우수상
2026 IEEE CTSoc Outstanding Service Award
2025 한국이공학진흥원 도슨트 상
2024 한국공학교육학회 한송엽 공학교육상
2023 과학기술정보통신부 장관 표창
2023, 2021, 2019, 2018, 2014, 2007 연세대 우수강의교수상
2023 연세대학교 공헌 교수상
2022 행정안전부 재난안전 연구개발성과 장려상
2022 한국공학한림원 (NAEK) 해동상
2022, 2021, 2017 한국인터넷정보학회 학술상
2021 행정안전부 최우수상 장관상
2020 대한전자공학회 공로상
2020 한국인터넷정보학회 공로상
2019 행정안전부 장관상
2019 경찰청 청장 표창 감사장
2019, 2018, 2008 연세대학교 우수업적 교수상
2017 한국인터넷정보학회 학술상
2017 국방부 합동참모본부 의장 표창 감사장
2016 대한전자공학회 공로상
2014 한국인터넷정보학회 공로상
2012 방위사업청 청장 표창
2012, 2010, 2007 한국통신학회 학술대회 우수논문상
2009 연세대학교 영어강의 우수교원상
2005 Regents Distinguished Research Award (USA)
2005 Halliburton Award of Excellence (USA)
2004 Technology Innovator Award (USA)
2003 Top Gun Award (USA)
2003 Distinguished Faculty Award (USA)
2000 IEEE EIT 2000, First Place Outstanding Paper Award (USA)
Relation between AI, ML, and DL
AI (ARTIFICIAL INTELLIGENCE)
Automation became
possible due to AI
technology
1950
1960
1970
ML (MACHINE LEARNING)
Machines become
capable of automated
learning and making
decisions
1980
1990
2000
DL (DEEP LEARNING)
Precision details are
cognitively noticed in the
automated learning process
and used in accurate
decision making of complex
problems
2010
Michael Copeland, "What’s the Difference Between Artificial Intelligence, Machine Learning,
and Deep Learning?," Nvidia, July 29, 2016. https://blogs.nvidia.com/blog/2016/07/29/whats
-difference-artificial-intelligence-machine-learning-deep-learning-ai/
Donarreiskoffer
https://commons.wikimedia.org/wiki/
File%3AGo_board.jpg
AI (Artificial Intelligence) Technologies (selected)

Foundational AI = Fixed architecture
•

Evolutionary Algorithms, Genetic Algorithms, Fuzzy Logic, Game Theory
Machine Learning = Adaptive AI  AI system can learn (limited range)
•
Machine Learning Basic
-
•
SVM (Support Vector Machine), Linear Regression, Logistic Regression, Naive Bayes,
Decision tree, k-means, P-value, etc.
Deep Learning (Deep Neural Networks)  Enhanced AI learning & expanded memory
-
CNN (Convolutional Neural Networks), RNN (Recurrent Neural Networks)
-
Deep Reinforcement Learning (Deep Learning + Reinforcement Learning)
-
Generative AI
∙
GAN (Generative Adversarial Network), ARM (Autoregressive Model), Transformer (GPT)
•
Federated Learning (a.k.a., Collaborative Learning) & Transfer Learning
•
Agentic AI (a.k.a., AI Agent)
Advanced AI  AGI (Artificial General Intelligence)
•
DRL (Deep Reinforcement Learning)
•
•
•
GenAI (Generative AI)
•
VAE (Variational AutoEncoder), ARM (AutoRegressive Model),
•
FM (Flow Model), DM (Diffusion Model), GAN (Generative Adversarial Network), etc.
•
DALL-E, PARTI, GPT, GPT-2, GPT-3, ChatGPT, GPT-4, GPT-5.4, Claude OPUS 4.6, etc.
Agentic AI (AI Agent)
•
•
RAG (Retrieval Augmented Generation), ReAct (Reasoning and Acting), etc.
FL (Federated Learning), CL (Collaborative Learning), TL (Transfer Learning)
•
•
DQN (Deep Q-Network) and DDPG (Deep Deterministic Policy Gradient)
Centralized FL, Decentralized FL, Heterogeneous FL
DT (Digital Twin)
•
DTP (DT Prototype), DTI (DT Instance), DTA (DT Aggregate)
EEE2113 ECE101
Machine Learning
Jong-Moon Chung, Ph.D., IEEE Fellow, NAEK
Professor, School of Electrical & Electronic Engineering
Professor, Department of Emergency Medicine, College of Medicine
Associate Dean, College of Engineering, Yonsei University
Vice President of IEEE PSES, Director of IEEE CTSoc
Machine Learning Algorithms
 ML Algorithms
 Basic statistics
•
Correlation
•
Hypothesis testing (P-value)
 Classification and Regression
•
Linear models
- SVM (Support Vector Machine)
- Linear Regression, Logistic Regression
•
Naive Bayes
•
Decision tree
Machine Learning Algorithms
 ML Algorithms
 Others
•
Collaborative Filtering
•
Clustering
- k-means
•
Dimensionality Reduction
•
etc.
Machine Learning Algorithms
 Correlation
 Variables can have a relationship of being
•
Independent
•
Weakly Correlated
•
Strongly Correlated
 Correlation indicates the extent to which variables have an
influence on each other (to increase or decrease)
Machine Learning Algorithms
 Correlation Example
 In the case of blue dots, 𝑥 and 𝑦 are strongly correlated
 On the other hand, the red dots show a weaker correlation
between 𝑥 and 𝑦
𝒚
𝒚
𝒙
𝒙
Machine Learning Algorithms
 Hypothesis Testing
 Hypothesis testing method
1. Define a statistic that obeys a certain distribution if the
hypothesis is correct
2. Collect samples and then calculate the statistic probability
3. If the sample statistics show a
probability lower than the threshold
of being drawn from this distribution,
then the hypothesis is rejected
Machine Learning Algorithms
 Hypothesis Testing Example
 If the probability is lower than the threshold Φ
(i.e., t is larger than Ω) then the hypothesis is rejected
𝑷𝒓𝒐𝒃.
Φ
Ω
𝒕
Reject the
Hypothesis
Machine Learning Algorithms
 Linear Models
 Frequently used in Regression
•
SVM (Support Vector Machine)
•
Logistic Regression
•
Linear Regression, etc.
 Regression is a method to predict the value of one (or more)
continuous target variable y given a (D-dimension vector) input x
Machine Learning Algorithms
 Linear Regression
 Linear regression is the simplest regression model
 Output of Linear regression is continuous
 To obtain output 𝑦, a linear combination of
input variables 𝑥 and weights 𝑤
𝑦(x,w) = 𝑤
𝑤 𝑥
⋯
𝑤 𝑥
Machine Learning Algorithms
 Linear Regression Example
 Example of linear regression
•
Red dots are input data values
•
Blue curve is the Linear regression output model
𝒚
𝒙
Machine Learning Algorithms
 Logistic Regression
 Used in classification problems that need to make a decision
 Decide among two options
•
Example
- Decide 0 or 1 using a Sigmoid curve
 Decide among multiple options
Machine Learning Algorithms
 Logistic Regression Example
 Sigmoid 𝝈(x) S-shape curve with a decision boundary
 Sigmoid functions are frequently used to make a decision
Small changes
on 𝝈(x) near
the decision
boundary will
determine the
classification
result of 0 or 1
1
𝝈 x
•
0
Classified to 1
Classified to 0
x
Machine Learning Algorithms
 SVM (Support Vector Machine)
 SVM technique is frequently used in solving problems in
classification, regression, and novelty detection
 SVM algorithm creates a decision boundary that
maximizes the margins between the groups that are being
classified
Machine Learning Algorithms
 SVM Example
 𝐇𝟏 represent another algorithm
•
Example: ML (Maximum Likelihood) algorithm
 𝐇𝟐 represents the SVM
 SVM 𝐇𝟐 attempts to
separate datasets
with the largest
margin
𝐇𝟏
𝐇𝟐
𝒚
Margin
𝒙
Machine Learning Algorithms
 Naive Bayes Classifier
 Conditional independence is assumed to simplify the classification
decision
 Bayes Theory is based on conditional probability
•
P(x|y,z) is the conditional probability that
x occurs based on the condition that y and z occurred earlier
•
If x is independent of z then P(x|y,z)  P(x|y)
- P(x|y,z) = P(x,y,z)
P(y,z)
- P(x|y) = P(x,y)
P(y)
Machine Learning Algorithms
 Naive Bayes Classifier Example
 Compute the probability of occurrence of dataset x through class c
•
𝑝 𝐱|𝑦
c = 𝒑 𝒙𝟏 ∙∙∙𝒙𝑫 |𝒚
c)
 “Naively” assume conditional independence between the features
c
∏𝑫
𝒋 𝟏 𝒑 𝒙𝒋 |𝒚
•
𝑝 𝐱𝑦
c = 𝒑 𝒙𝟏 |𝒚
•
D is the number of features
c)∙∙∙ 𝒑 𝒙𝑫 |𝒚
c)
Machine Learning Algorithms
 Naive Bayes Classifier Example
𝑫
𝑝 𝐱𝑦
c
Multi-dimension
(D dimensions)
𝒑 𝒙𝒋 |𝒚
c
𝒋 𝟏
Product of one dimensional densities
 Multi-dimensional probability is easily
obtained from multiplications (product)
of many one dimensional densities
 Approximation results in less accurate
results, but enables output in real-time
Machine Learning Algorithms
 Naive Bayes Classifier Example
𝑫
𝑝 𝐱𝑦
c
Multi-dimension
𝒑 𝒙𝒋 |𝒚
c
𝒋 𝟏
Product of one dimensional densities
 This model is called “naive”
since in reality these features
are not independent
 But it often works very well
 Error analysis required
Machine Learning Algorithms
 Decision Tree
 Local region is identified (classified) in a sequence of
recursive splitting decisions in an efficient way
•
Fewer number of processing steps
•
Each step has low computation
•
Results in a hierarchical tree shape form
 Hierarchical algorithm is used in supervised learning (training)
of the Decision Tree
Machine Learning Algorithms
 Decision Tree Example
 For classification, decision boundaries on the dataset
(white and red dots) can be determined using a
decision tree
𝒚
𝒙
𝐇𝟏
yes
no
𝐇𝟐
𝐲
yes
𝐇𝟏
𝐇𝟐
no
𝒙
Machine Learning Algorithms
 Collaborative Filtering
 ML algorithm that collects preferences or taste information
from many users (collaborative) and uses this information to
make automatic predictions (filtering) about the interests of
other users
•
Collaborative
- Combining collected information
•
Filtering
- Filter out less probable options to find
the most probable prediction
Machine Learning Algorithms
 Collaborative Filtering Example
 Music vendor can recommend music to a New User based on
information on User A who’s characteristics seem similar
like
similar
User A
Music A
Music B
recommend
Music C
New User
Machine Learning Algorithms
 Clustering
 Process of finding similar characteristics in a dataset to
form groups of data
 Training data consists of a set of input vectors without
any corresponding target values
•
Dataset contains no information (labels) on data and cluster
relation
•
Unsupervised Learning is needed
Machine Learning Algorithms
 k-means Algorithm
 One of the most famous clustering (classification) algorithms
 Unlabeled data is classified to k classes
 Mean (average) of each class is updated when new data
(vector) is received
 Mean value is used to update the division of the classes
(clusters)
Machine Learning Algorithms
 k-means Algorithm Example
 Classify unlabeled data into the two classes
 Red or White
•
All data (vector) is originally colorless
Machine Learning Algorithms
 k-means Algorithm Example
 Step 1: Decide the two centers of the classes randomly
 Step 2: Allocate the data into the nearest center
Machine Learning Algorithms
 k-means Algorithm Example
 Step 3: Calculate a mean of each class based on the
average distance
 Step 4: Allocate the data into the nearest center
Machine Learning Algorithms
 k-means Algorithm Example
 Step 5: Calculate a mean of each class again based on the
average distance
 Final Step: Allocate the data (vector) into the
nearest center’s class
Machine Learning Algorithms
 Dimensionality Reduction
 Reduces dimensionality by projecting the dataset to
a lower dimensional subspace
•
Captures the essence of the dataset
•
Reduces the complexity of the classifier and regressor
- Complexity depends on the number of inputs
- Both the time and space complexity is considered
Machine Learning Algorithms
 Dimensionality Reduction Example
 Drone management map generation
 3D (Longitude, Latitude, Altitude) Info
Longitude  x, Latitude  y, Altitude  z
•
 2D (Longitude, Latitude) Drone Map
𝒛
𝒛
𝒚
𝒚
𝒙
𝒙
Machine Learning Algorithms
 Dimensionality Reduction Example
𝒛
𝒛
𝒚
𝒚
𝒙
𝒙
𝒓𝒆𝒎𝒐𝒗𝒆
𝒛
𝒚
𝒙
𝒚
𝒙
Machine Learning Algorithms
 Dimensionality Reduction - Example
 Assuming that altitude information z is not needed
 By eliminating z from data, we can capture the essence
of the data
𝒓𝒆𝒎𝒐𝒗𝒆
𝒛
𝒚
𝒚
𝒙
𝒙
Machine Learning Algorithms
 ML Algorithms Summary
 Basic statistics
•
Correlation, Hypothesis testing (P-value)
 Classification and Regression
•
Linear models (SVM, Linear & Logistic Regression), Naive Bayes,
Decision tree
 Others
•
Collaborative Filtering, Clustering (k-means), Dimensionality
Reduction, etc.
EEE2113 ECE101
Artificial Neural Networks
and Deep Learning
Jong-Moon Chung, Ph.D., IEEE Fellow, NAEK
Professor, School of Electrical & Electronic Engineering
Professor, Department of Emergency Medicine, College of Medicine
Associate Dean, College of Engineering, Yonsei University
Vice President of IEEE PSES, Director of IEEE CTSoc
Neural Network
Neuron (Nerve Cell)
Neural Network
Neuron Signal Transfer
Direction Message Travels
Synaptic Connections
Axon
Terminals
Dendrites
Human’s Brain has 100 Billion
neurons with 1 Quadrillion (1015)
synaptic connections
Deep Learning & Machine Learning
Neuron with Threshold Logic Unit
― W. McCulloch & W. Pitts (1943) model
Summation adds up to y
Activation
Function
Inputs
Weights
Inputs x Weights
Output=
1
if 𝒚 > T
0
Otherwise
Big Data Intelligence, DL & ML
Soft Output Activation Functions
― Logistic sigmoid
𝝈 𝒚
― Softplus
𝜻 𝒚
𝟏
𝟏
𝐞𝐱𝐩
𝐥𝐨𝐠 𝟏
𝒚
𝐞𝐱𝐩 𝒚
― ReLU (Rectified Linear Unit)
𝒈 𝒚
𝐦𝐚𝐱 𝟎, 𝒚
Neural Network Terminology
Perceptron
― ML (Machine Leaning) algorithm that conducts supervised
learning of linear (binary) classification
― Perceptrons are trained to determine if an input (vector or
scalar value) belongs to one class or another
Neural Network Terminology
MLP (Multi-Layer Perceptron)
― Feed forward NN (Neural Network) that is formed of multiple
layers of perceptrons, where each layer may use multiple
perceptrons in parallel
― MLPs use BP (Back Propagation) based supervised learning to train
the outputs (to accurately conduct nonlinear classification)
Neural Network Terminology
SoftMax
― Logistic function that maps a K dimensional vector
(e.g., a set of K data inputs) of real values to
values in the range of 0~1 such that
all values of the vector add up to 1
― ML (Machine Learning) NNs (Neural Networks)
often use the Softmax function to enhance the
accuracy of the classification process
Neural Network Terminology
SoftMax transfer example
― Original Inputs: 𝑎 , 𝑎 , 𝑎
𝑎
― SoftMax Outputs
𝑎
𝑎
― Constraint Satisfied: ( 𝑎 + 𝑎 + 𝑎 ) = 1
Neural Network Terminology
SoftMax transfer example
― a1=9
a2=20
a3=25

ã1=0.00000011178
ã2=0.00669285018
ã3=0.99330703804
― (𝑎 +𝑎 +𝑎 )=1
― Weights (𝑤 , 𝑤 , 𝑤 ) and SoftMax values (𝑎 , 𝑎 , 𝑎 ) combined
𝑤 𝑎 +𝑤 𝑎 +𝑤 𝑎
Neural Network Learning
How many neurons and in what structure?
― Weights that need to be trained
•
Single layer with weights: w1 w2
w1
w2
A
B
C
D
Structure
Output
Martin T. Hagan, Howard B. Demuth, Mark H. Beale, Orlando De Jesús, Neural
Network Design, 2nd Ed. ISBN-13: 978-0971732117, Martin Hagan, Sept. 1, 2014.
Neural Network Learning
How many neurons and in what structure?
― Weights that need to be trained
•
Two layers with weights: w1 w2 w3 w4
w1
w2
w3
w4
Structure
A
B
C
D
Output
Neural Network Learning
How many neurons and in what structure?
― Weights that need to be trained
• Three layers with weights: w1 w2 w3 w4 w5 w6 w7 w8
w1
w3
w2
w5 w6
w7
w4
A
B
C
D
w8
Structure
Output
Neural Network Learning
Neural Network Layers
Weight Training
Neural Network Learning
Neural Network Layers
― Input Layer
• Layer where the input to the NN (Neural Network) comes in
― Output Layer
• Layer where the output of the NN goes out and is used
Neural Network Learning
Neural Network Layers
― Hidden Layer
• Layer that contains the intelligence in a distributed fashion using many
neurons, interconnections, weights, biases, activation functions, etc.
― Multiple hidden layers can be used
― Deep NNs have many hidden layers
Neural Network Learning
Deep Neural Network
― Deep NNs have many hidden layers
• How many hidden layers are needed to
(qualify to) be called “Deep”?
Learning Methods
Learning Methods
― How do we train the weights of these layers to make the NN
Intelligent?
― Supervised Learning
• Training that uses labeled data (desired outputs)
― Unsupervised Learning
• Training that uses unlabeled data (no desired outputs are used)
Learning Methods
Learning Methods
― Semi-supervised Learning
• Training that uses both labeled data and unlabeled data
― Reinforcement Learning
• Feedback is given back into the system, but no labeled data is used
Learning Methods
Supervised Learning using Back Propagation Training
Back Propagate Errors to Train NN Weights
Current
Output
= Error
- Desired
Output
(Labeled
Data)
1. Forward propagate the training pattern’s input through the NN
2. NN generates initial output values
Learning Methods
Backpropagation Learning Algorithm
3. Use the Difference value and Input value to derive the Gradient
of the Weights of the output layer and hidden layer neurons
4. Scale down the Gradient of the Weights
(i.e., reduce the Learning Rate)
• Learning Rate determines the learning speed and resolution
Learning Methods
Backpropagation Learning Algorithm
5. Update weights in the opposite direction of the +/- sign of the
Gradient
• Because the +/- sign of the Gradient indicates +/- direction of the Error
6. Repeat all steps until the desired Input-to-Output performance
is satisfactory
Learning Methods
Supervised Learning using
Back Propagation Training
― Since each weight of the NN is used in the calculation of various
other inputs, the size of a weight update has to be small
in each training iteration
― This is because a big change in one weight may mess up the
weights that were trained to match the other input to output
values
EEE2113 ECE101
AI Foundational
Technologies &
Terminologies
Jong-Moon Chung, Ph.D., IEEE Fellow, NAEK
Professor, School of Electrical & Electronic Engineering
Professor, Department of Emergency Medicine, College of Medicine
Associate Dean, College of Engineering, Yonsei University
Vice President of IEEE PSES, Director of IEEE CTSoc
AI Foundational Technologies
 Game Theory (1944, 1950)
 Evolutionary Algorithms (1960s)
 Genetic Algorithms (1960s)
 Fuzzy Logic (1965)
Dandelion (민들레)
Dandelion Blowing - Trendy Poster - Photowall, Brand: Great Big Canvas
https://www.photowall.co.uk/dandelion-blowing-poster
Game Theory

1944, book "Theory of Games and Economic Behavior"
•
Authors John von Neumann and Oskar Morgenstern
Amazon https://www.amazon.com/Theory-Games-Economic-Behavior-Commemorative/dp/0691130612

Key Parts
•
Players: Decision-makers (individuals, firms, nations)
•
Strategy: Complete plan of action for a player
•
Payoff: Reward a player receives
•
Dominant Strategy: Most rewarding strategy for a player (regardless of what other players do)
•
Information type: Perfect, imperfect, or asymmetric
•
Nash Equilibrium
-
Manhattan Rare Book Company https://www.manhattanrare
books.com/pages/books/2722/john-von-neumann-oskar-mor
genstern/theory-of-games-and-economic-behavior
1950 (Jan. 15), John F. Nash, "Equilibrium points in n-person games," PNAS, 36(1), pp. 48-49 (2 pages)
Adam Smith vs. John F. Nash, Jr.


Adam Smith (1723-1790)
•
Individual self-interest based on competition can drive the
best outcome for the common good
•
"The Invisible Hand" is mentioned in the book "An Inquiry into
the Nature and Causes of the Wealth of Nations" (Publisher:
W. Strahan and T. Cadell, London, UK, 9 March 1776)
•
Does not ignore the affect of collective well-being
Wikipedia https://en.wikipedia.org/
wiki/Adam_Smith
Wikipedia https://en.wikipedia.o
rg/wiki/John_Forbes_Nash_Jr.
Amazon https://www.amazon.com/Beautif
ul-Mind-Sylvia-Nasar/dp/1451628420
Medium.com https://medium.com/@mtkhs39
/a-beautiful-mind-movie-and-nash-equil
ibrium-63c8b94dbcd3
John Forbes Nash Jr. (1928-2015)
•
Individuals pursuing only their own interest often lead
to sub-optimal and non-cooperative outcomes
•
Nash Equilibrium
-
1950 (Jan. 15), John F. Nash, "Equilibrium Points in N-person
Games," Proceedings of the National Academy of Sciences (PNAS),
36(1), pp. 48-49 (2 pages)
https://www.pnas.org/doi/epdf/10.1073/pnas.36.1.48
Game Theory

Nash Equilibrium
•
1950 (Jan. 15), John F. Nash, "Equilibrium Points in N-person Games,"
PNAS, 36(1), pp. 48-49. https://doi.org/10.1073/pnas.36.1.48
•
1994, John Nash receives the Nobel Memorial Prize in Economic Sciences
•
Key Aspects of the Nash Equilibrium
-
Nash Equilibrium
∙
-
NobelPrize.org https://www.nobelprize.
org/prizes/economic-sciences/1994/nash
/photo-gallery/?gallery_style=page
Non-Cooperative Games
∙
-
Stable outcome can be reached even if all participants are
greedy and only seek to optimize their own, local, immediate payoffs
Includes effect of game players that cannot make binding agreements (e.g., Prisoner's Dilemma)
Mixed Strategy Equilibria
∙
Proven that in any finite game, there always exists at least one equilibrium, even if it requires
players to randomize their actions (mixed strategies)
Evolutionary Algorithms

1960s, from several researchers inspired by Darwinian principles of natural selection

Population-based, stochastic optimization methods inspired by natural biological
evolution (selection, recombination, crossover, mutation)

Used to derive candidate solutions for complex, non-linear optimization problems

Key Components
•
Population: Diverse initial set of candidate solutions
•
Fitness Function: Evaluates how well a solution performs to determine its chances of survival
•
Selection: Chooses the best individuals (fittest ones) to reproduce (mimics natural selection)
•
Crossover (Recombination): Combines parts of two parent solutions to create new offspring (e.g.,
genetic algorithms)
•
Mutation: Introduces random changes to individuals to maintain genetic diversity and explore new
solution spaces
•
Termination: Stops the process after a maximum number of generations, or when a satisfactory solution
is found (exceeding threshold)
Evolutionary Algorithms



Types of Evolutionary Algorithms
•
GA (Genetic Algorithm): Uses binary string representations of genes and mutations
•
ES (Evolution Strategies): Focuses on behavior changes (rather than genetic changes)
•
EP (Evolutionary Programming): Uses creation of new behaviors (similar to ES without crossovers)
•
MA (Memetic Algorithm): Hybrid algorithm combines population-based search and local search
Pros
•
Efficient in finding solutions in complexity, non-linear, or poorly understood data
•
Does not require gradient information of the optimization function (can solve noisy & discontinuous
problems)
•
Cost-effective operation (can run on a CPU) and resource saving (energy, cost CAPEX & OPEX)
Cons
•
May not be able to find the optimal solution
•
May find the optimal solution but may not realize it
Genetic Algorithms

1960s, prof. John Holland (University of Michigan, IEEE Fellow)

Can find near-optimal solutions by evolving a population of candidate solutions
over generations

Key Procedures
•
Initialization: Generate a random population of potential solutions (chromosomes)
•
Fitness Function: Evaluated how well each individual solves the problem based on the
Fitness scores
•
Selection: Reproduce using the Fitter individuals
•
Crossover (Recombination): Offspring are created from gene combination of parents
•
Mutation: Diversity added using random small changes
•
Repetition: Repeat until a satisfactory solution is found, or time expires
Fuzzy Logic & Algorithm

1965, proposed by prof. Lotfi A. Zadeh (UC Berkeley, IEEE Fellow, z transformation)

Uses "degrees of truth" rather than binary (true/false) logic to allow gray zone analysis
(between 0 and 1)

Human reasoning model to enable control with data that is imprecise, vague, or nonnumerical information

Fuzzification
•

Rule Evaluation
•

Uses membership functions to convert input data into fuzzy sets (e.g.., "high" = 0.8)
Applies fuzzy IF-THEN rules to determine the output (can be partially true)
Defuzzification
•
Converts the resulting fuzzy output back into a specific action value (e.g., control altitude to 0.7)
Fuzzy Logic & Algorithm


Pros
•
Handles Uncertainty: Ideal for complex, imprecise, or hard-to-model systems
•
Human-Readable Rules: Uses natural language terms (e.g., fast, slow, cold, warm, hot, etc.)
•
Flexibility: Can easily accommodate new rules or modify existing ones
Cons
•
System Complexity: Challenging to design and tune membership functions and rules
•
Verification: Difficult to verify and validate the accuracy compared to traditional systems
•
Computation Speed: Complex models may require more processing time and resources
Deep Learning & Machine Learning
CPU (Central Processing Unit)
― CPU executes instructions of a PC, Smartphone, etc.
― Designed to support all process types
GPU (Graphics Processing Unit)
― GPU is a custom-made CPU that is operation specified for high
speed and low power operations
― Embedded in PC and Smartphone Video Cards, Motherboards, and
inside CPUs
Deep Learning & Machine Learning
Computer Performance Units
― FLOPS (FLoating-Point Operations Per Second)
― Floating-point computation based performance measure
― GFLOPS (Giga=Billions=109 FLOPS) commonly used
― IPS (Instructions per Second)
― Integer number of operations-based performance measure
― MIPS (Millions=106 of IPS) commonly used
Deep Learning & Machine Learning
FLOPS Comparison (Approximations)
― Human Brain (≈20% of body)
• 0.01 FLOPS with 2.5 x 1015 = 2.5 PB of
memory running on 12~20 W of power
― Modern Smartphones or PCs
• 10~300 GFLOPS (109 FLOPS)
― Google’s DeepMind AlphaGo Master
• TPU 2G 11.5 PFLOPS = 11.5x1015 FLOPS
Scalar  Vector (Matrix)  Tensor
•
1D (1 dimensional): Sequence scalar value data
representing text, numbers, acoustic signal, etc.
•
2D (2 dimensional) or 3D (3 dimensional) data
can be represented using a 2D or 3D vector
in matrix form
•
4D and higher dimensional data points
can be formed from a combination
(a, b, c) of multiple dimensional
components called a Tensor
•
Tensor Flow, TPU (Tensor Processing Unit)
Hopfield Neural Network
•
Structure
•
•
•
•
The network uses an iterative update rule
•
Random neuron is chosen to update its state based on the weighted
sum of inputs from other neurons compared to a threshold
Energy Function
Attempts to minimize the energy function
Convergence
•
•
Functions as a content-addressable memory
(can retrieve a full pattern when provided with only a part)
Dynamics
•
•
Single layer that connects every neuron to every other neuron
(no self-connections)
Associative Memory
•
•
J. J. Hopfield, "Neural networks and physical systems with emerg
ent collective computational abilities," PNAS, 79(8), pp. 2554-255
8, Jan. 15, 1982 (https://doi.org/10.1073/pnas.79.8.2554)
Always converges to a stable state
(a local minimum of the energy profile)
Training (Weight Setup)
•
Weights are set and controlled using Hebbian learning rules
•
Connections between neurons that collaborated (fire) together
are strengthened
Hopfield Neural Network
•
1982, prof. John Hopfield (CalTech,
Bell Labs, Princeton University)
•
2024 Nobel Prize in Physics
•
Recurrent artificial neural network based
associative, content addressable memory
system
•
Applications
•
Pattern Recognition & Restoration
•
•
Denoising images or recovering partial data
Optimization Problems
•
Can help solve complex constraint-based
optimization problems
Princeton University https://www.facebook.com/PrincetonU/videos/
john-hopfield-receives-nobel-prize-in-stockholm/594649839600209/
EEE2113 ECE101
Deep Learning
with CNN
(Convolutional Neural Network)
Jong-Moon Chung, Ph.D., IEEE Fellow, NAEK
Professor, School of Electrical & Electronic Engineering
Professor, Department of Emergency Medicine, College of Medicine
Associate Dean, College of Engineering, Yonsei University
Vice President of IEEE PSES, Director of IEEE CTSoc
CNN (Convolutional Neural Network)
CNN Characteristics
― Deep Learning CNN techniques became
well known based on an outstanding
(winning) performance of image recognition at the
ImageNet Challenge 2012 ILSVRC https://www.image-net.org/challenges/LSVRC/
by Krizhevsky & Hinton from the
University of Toronto
― CNNs need a minimal amount of preprocessing
CNN (Convolutional Neural Network)
CNN Structure
(Pooling)
Filtering field sequentially
shifts around (convolution process)
to process the whole image
CNN (Convolutional Neural Network)
Convolutional Layer
― Convolution is used to find the same
feature in different places of an image
― Convolution is conducted using
learnable filters/kernels (that have
small receptive fields) that are passed
(convoluted) through the input
data/image
CNN (Convolutional Neural Network)
Convolutional Layer
― Each filter moves sequentially across (convolved) the input
data/image to make a 2-dimensional activation map based on
each filter
CNN (Convolutional Neural Network)
Feature Maps
― Feature Maps are made from the activation maps of the filters
― Number of Learnable filters/kernels
(and how the data, weight training, bias values, etc. are used)
in the Convolution process determines how many Feature Maps
are generated after Convolution
CNN (Convolutional Neural Network)
Subsampling
― Subsampling uses a selecting operation (pooling) on the
Feature Maps
― Subsampling is a non-linear down-sampling process that
results in smaller Feature Maps
― Most popular subsampling schemes (many exist)
• Median Value
• Average Value
• Max Pooling
CNN (Convolutional Neural Network)
Subsampling: Median Value
― For each sub-region select the median value (middle value)
as the representative sample
Among 1, 2, 5, 8 the
average of 2 and 5 is 4
5
1
4
2
8
2
6
9
4
5
1
0
2
4
2
4
3
7
4
8
CNN (Convolutional Neural Network)
Subsampling: Median Value
― For each sub-region select the median value (middle value)
as the representative sample
5
1
4
2
8
2
6
9
4
5
1
0
2
4
2
4
3
7
4
8
Among
2, 4, 6, 9
the average of
4 and 6 is 5
CNN (Convolutional Neural Network)
Subsampling: Median Value
― For each sub-region select the median value (middle value)
as the representative sample
If there were an odd number of values,
then the middle one would be the
median value (e.g., 1, 4, 7, 8, 10  ?)
5
1
4
2
8
2
6
9
4
5
1
0
2
4
2
4
3
7
4
8
Among
2, 4, 6, 9
the average of
4 and 6 is 5
CNN (Convolutional Neural Network)
Subsampling: Median Value
― For each sub-region select the median value (middle value)
as the representative sample
If there were an odd number of values,
then the middle one would be the
median value (e.g., 1, 4, 7, 8, 10  7)
5
1
4
2
8
2
6
9
4
5
1
0
2
4
2
4
3
7
4
8
Among
2, 4, 6, 9
the average of
4 and 6 is 5
CNN (Convolutional Neural Network)
Subsampling: Average Value
― For each sub-region use the average value as the representative
Average
{(8+5+2+1)/4} = 4
CNN (Convolutional Neural Network)
Subsampling: Max Value
― Each sub-region selects its max value as its representative value
CNN Techniques
CNN Processing Characteristics
― Convolution layer increases number of feature maps
― Pooling (subsampling) layer deceases spatial resolution
CNN Techniques
LCN (Local Contrast Normalization)
― LCN improves the optimization results and the image’s invariance
― (i.e., characteristic of not changing after transformation or processing)
― LCN can be used on the image after convolution and before pooling
(subsampling)
CNN Techniques
Dropout
― On each iteration, selected neurons are randomly turned off
•
Based on a probability model
― Helps in training neurons to properly work even when other
neurons may not exist (neuron failure)
•
Provides robustness to the CNN
CNN Techniques
Full Connected MLP Output Layer
― Output layer uses a fully connected MLP (Multi-Layer Perceptron)
to the previous hidden layer
― Outputs are computed with a matrix multiplication and biased
offset
CNN Techniques
Ensemble
― Ensembles are created by repeated random sampling of the training
(labeled) data
― Ensembles improve the accuracy and reliability by providing an
improved global image of the data’s actual statistics
― Ensemble models are often used in Deep NNs
CNN Techniques
Bagging
― Bagging uses multiple iterations of training the CNN with
training/labeled data that has random sample replacements
― After training, the results of all trained models of all iterations
are combined
EEE2113 ECE101
Deep Learning
with RNN
(Recurrent Neural Network)
Jong-Moon Chung, Ph.D., IEEE Fellow, NAEK
Professor, School of Electrical & Electronic Engineering
Professor, Department of Emergency Medicine, College of Medicine
Associate Dean, College of Engineering, Yonsei University
Vice President of IEEE PSES, Director of IEEE CTSoc
RNN (Recurrent Neural Network)
RNN Applications
― Speech Recognition
• Apple's Siri
• Google’s Voice Search
• Samsung's S Voice
― Handwriting Recognition
― Sequence Data Analysis
― Program Code Generation
• RNN automatically generates computer programming code that can serve a
predefined functional objective
RNN (Recurrent Neural Network)
RNN Structure
― NN that uses directed cyclic connections between neurons
― Directed cyclic connections create internal states with dynamic
temporal characteristics
― Internal memory is used to process arbitrary input data sequences
― Sequence Modeling is used for data sequence Classification &
Clustering
RNN (Recurrent Neural Network)
RNN Structure
― Sequence Modeling structure based on
S2S (Sequence to Sequence) Learning
•
N inputs are transformed into M outputs
y1
y2
y3
y4
𝑴
x6
𝑵
𝟒 𝒐𝒖𝒕𝒑𝒖𝒕𝒔
Output 𝒀
S2S Learning
x1
x2
x3
x4
x5
Input 𝑿
𝟔 𝒊𝒏𝒑𝒖𝒕𝒔
RNN (Recurrent Neural Network)
RNN Process
1. Data input to the Input Layer
2. Representation of the data in the Input Layer is computed and
sent to the Hidden Layer
3. Hidden Layer conducts Sequence Modeling and Training
in Forward or Backward directions
RNN (Recurrent Neural Network)
RNN Process
4. Multiple Hidden Layers using Forward or Backward direction
Sequence Modeling and Training can be used
5. Final Hidden Layer sends the processed result to the Output Layer
RNN (Recurrent Neural Network)
Forward RNN
Output Layer
Related to Memory
Hidden Layer
RNN Layer
X1
X2
X4 Sequence Modeling
X3
Input Layer
RNN (Recurrent Neural Network)
Backward RNN
Output Layer
Hidden Layer
RNN Layer
X1
Input Layer
X2
X3
X4
RNN (Recurrent Neural Network)
S2S (Sequence to Sequence) Deep Learning RNN
― Assuming an input data sequence of Data 1, Data 2, Data 3, …
applied to the RNN, a Hidden Layer may be in the form of a
Forward RNN or Backward RNN
…
…
RNN Layer X
RNN Layer 2
RNN Layer 1
…
…
Input Layer
Hidden Layers
Output Layer
RNN (Recurrent Neural Network)
RNN Processes
― Representation
• Applied to the Input Layer data
• Similar to Subsampling (Pooling) in CNN
• Representation is used to extract the important data that
characterizes the data set in the best way
• Representation is a non-linear down-sampling process
RNN (Recurrent Neural Network)
Representation Techniques
― Median
― Center
Center value is selected
•
10
1
11
2
5
13
9
3
7
After lining up the data sequence from
Largest to Smallest, the middle value
is selected
•
13, 11, 10, 9, 7, 5, 3, 2, 1
5
10
1
11
2
5
13
9
3
7
7
RNN (Recurrent Neural Network)
Representation Techniques
― Average
― Max Pooling
Average value is used
•
(10+1+11+2+5+13+9+3+7)/9 ≅ 6.7
10
1
11
2
5
13
9
3
7
6.7
Max value is selected
•
1, 2, 3, 5, 7, 9, 10, 11, 13
10
1
11
2
5
13
9
3
7
13
RNN (Recurrent Neural Network)
Representation Techniques
― Weighted Sum
• Data values d1, d2,…, d9 are scaled by weights w1, w2,…, w9 to create the
weighted sum v = (w1d1 + w2d2 +…+ w9d9)
w1
w2
w3
d1
d2
d3
w4
w5
w6
d4
d5
d6
w7
w8
w9
d7
d8
d9
𝒗
𝒘𝟏 𝒅𝟏
𝒘𝟐 𝒅𝟐
⋯
𝒘𝟗 𝒅𝟗
𝒗
Element-wise multiplication
RNN (Recurrent Neural Network)
Context based Projection
― Use assisting data (context)
in the representation process
WC
― Context data may be the
Original Input data or
some type of Biased or
Transformed data
c1
W
Representation
c2
x1 x2 x3
c3
x4 x5 x6
c4
x7 x8 x9
Context
Data
RNN (Recurrent Neural Network)
Context based Projection
Current Input (Data 3)
& Past Information
Context
WC
WC
W
W
1
2
3
Data 1
Data 2
Data 3
RNN (Recurrent Neural Network)
Representation with Attention
― Attention enables the decoder to attend to different parts
of the source data segment at various steps of the output
generation
― Language Translation Example
•
RNN attends to sequential input states, multiple words
simultaneously, and words in different orders when producing
the output translated language
RNN (Recurrent Neural Network)
Representation with Attention
― SoftMax transfer
( ã1 + ã2 + ã3 ) = 1
a1 → ã1
a2 → ã2
a3 → ã3
― Attention values (A1, A2, A3) represent the importance of the data set
x1
A1
x2
A2
x3
A3
Attention
xs
RNN (Recurrent Neural Network)
Representation with Attention
― SoftMax values (ã1, ã2, ã3) and
Weights (w1, w2, w3) and
Attention values (A1, A2, A3) are used to transform the values
• 𝒂𝟏
𝒆𝒂 𝟏
𝒆𝒂𝟏 𝒆𝒂𝟐 𝒆𝒂𝟑
• 𝒂𝟐
𝒆𝒂 𝟐
𝒆𝒂𝟏 𝒆𝒂𝟐 𝒆𝒂𝟑
• 𝒂𝟑
𝒆𝒂 𝟑
𝒆𝒂𝟏 𝒆𝒂𝟐 𝒆𝒂𝟑
( ã1 + ã2 + ã3 ) = 1
― w1ã1 and w2ã2 and w3ã3 are used to transform each data set
RNN (Recurrent Neural Network)
Representation with Attention
Example
1
2
3
A1
A2
A3
𝒘𝟏 𝒂𝟏
1′
𝒂𝟐
𝒘𝟐 𝒂𝟐
2′
𝒂𝟑
𝒘𝟑 𝒂𝟑
3′
𝒂𝟏
SoftMax
XS
Element-wise
Multiplication
RNN (Recurrent Neural Network)
FRNN (Fully Recurrent NN)
― RNN early model
― All neurons have connections to other neurons with modifiable
weights
― Neurons form Input, Hidden, and Output Layers
RNN (Recurrent Neural Network)
LSTM (Long Short-Term Memory) RNN
― Very popular RNN model
― LSTM Cell has an Input Gate,
Output Gate, Forget Gate, and 𝒙𝒕
Self-Loop (may have more gates)
Input
Input Gate
State
Forget Gate
Self-loop
+
𝒉𝒕
Output Gate
― Self-Loop in the LSTM Cell gives
a data sequence memory effect
LSTM RNN Cell Structure Example
RNN (Recurrent Neural Network)
LSTM (Long Short-Term Memory) RNN
― RNN Recurrent Gate (Forget Gate) effect
• Prevents backpropagated errors from
Vanishing (Vanishing Gradient Problem)
or Exploding (Divergence Problem)
• LSTM Recurrent Gates (Forget Gates) enable Errors
to flow backwards through an unlimited number of
VL (Virtual Layers) extending the memory
characteristics
RNN (Recurrent Neural Network)
LSTM RNN Characteristics
― LSTM is effective on data sequences that require memory of
far past events
(e.g., thousands of discrete time steps ago)
― LSTM RNNs perform well on data sequences with long delays,
and mixed signals with high and low frequency components
RNN (Recurrent Neural Network)
LSTM RNN Applications
― Speech Recognition and
Large-Vocabulary Speech Recognition
― Pattern Recognition
― Connected Handwriting Recognition
― Text-to-Speech Synthesis
RNN (Recurrent Neural Network)
LSTM RNN Applications
― Recognition of Context Sensitive Languages
― Machine Translation
― Language Modeling
― Multilingual Language Processing
― Automatic image captioning (using LSTM RNN + CNN)
EEE2113 ECE101
ILSVRC (ImageNet
Large Scale Visual
Recognition Challenge)
Jong-Moon Chung, Ph.D., IEEE Fellow, NAEK
Professor, School of Electrical & Electronic Engineering
Professor, Department of Emergency Medicine, College of Medicine
Associate Dean, College of Engineering, Yonsei University
Vice President of IEEE PSES, Director of IEEE CTSoc
ILSVRC ImageNet Challenge
ILSVRC https://www.image-net.org/challenges/LSVRC/
ILSVRC (ImageNet Large Scale
Visual Recognition Challenge)
• Annual contest started in 2010
• Object category classification & detection
• 3 main challenges used to benchmark large-scale object
recognition capability
• 1 Object localization (top-5)
• 2 Object Detection challenges
• Images & Videos
ILSVRC ImageNet Challenge
1. Object Localization (top-5)
• The original competition of ILSVRC
• The other two Object Detection challenges
were included into ILSVRC later
• Training data set: 1.2 million images
• Labeled object categories: 1,000 categories
• Test image set: 150,000 photographs
• Each competing program lists its top 5 confident labels based on
each test image in decreasing order of confidence and
bounding boxes for each class label
ILSVRC ImageNet Challenge
1. Object Localization (top-5)
• Evaluated based on accuracy of the program’s localization
labeling results, the test image’s ground truth labels,
and object in the bounding boxes
• Program with the minimum average error is the winner
ILSVRC ImageNet Challenge
ILSVRC top-5 Example
― ILSVRC Object
localization
challenge
(top-5)
example
―
Top-5 selections
for each image
listed with
probability
histograms
ScienceDirect https://www.sciencedirect.com/topics/engineering/image-classification
ILSVRC https://www.image-net.org/challenges/LSVRC/
ILSVRC ImageNet Challenge
2. Object Detection - Images
• Each program attempts identification of 200 basic-level
categories
• Test image data set has fully annotated labels
(for each bounding boxed object) on each image
• Winner is the program with the highest accuracy in annotated
class labels, confidence scores, and bounding boxes
ILSVRC ImageNet Challenge
3. Object Detection - Videos
• Contender program attempts identification of 30 basic-level
categories
• Subset of the 200 basic-level categories
• Each program will produce a set of annotations of frame number, class labels,
confidence scores, and bounding boxes for the video clip
• Winner is the program with the highest accuracy on the most object
categories
ILSVRC ImageNet Challenge
Participant’s Program Requirements
• Each team is allowed 2 submissions per week
• No regulation on the number of neural network layers
• Learning scheme and parameters
have to be based only on the training set
ILSVRC ImageNet Challenge
ILSVRC Annual Results
• Significant improvements in performance have been seen since
Deep Learning has been used in 2012
AlexNet
28.2 neural network used for the first time
30 Learning)
• DL (Deep
25.8
ImageNet Classification top-5 error (%)
25
20
15
10
5
8 layers
16.4
8 layers
11.7
8 layers
22 layers
7.3
6.7
152 layers
5.1
3.5
0
ILSVRC 2010 ILSVRC 2011 ILSVRC 2012 ILSVRC 2013 ILSVRC 2014 ILSVRC 2014 Human ILSVRC 2015
NEC America
Xerox
AlexNet
Clarifi
VGG
GoogleNet Performance ResNet
ILSVRC ImageNet Challenge
2012 Winner AlexNet
• Created by Alex Krizhevsky, Ilya Sutskever,
and Geoffrey Hinton (University of Toronto)
• Results
• 2011 Xerox  2012 AlexNet
74.2%
83.6%
• Significant 9.4% improvement in the performance in the
Object Localization (top-5) competition
ILSVRC ImageNet Challenge
2012 Winner AlexNet
• System Characteristics
• DL (Deep Learning) was used for the first time
• 8 layer DL neural network
• 5 convolution layers
• 3 fully connected layers
• 60 million parameters
• Trained for 6 days
• Used two Nvidia GTX-580 with 3GB memory
ILSVRC ImageNet Challenge
2014 Winner GoogleNet (Inception-v1)
• Results
• 93.3% accuracy in the Object Localization (top-5) competition
• 90% accuracy exceeded for the first time
• System Characteristics
• 22 layer DL neural network with 5 million parameters
• Trained for 1 week on the Google DistBelief cluster
ILSVRC ImageNet Challenge
2015 Winner Microsoft ResNet
• Results
• 96.5% accuracy in the Object Localization (top-5) competition
• 94.9% human accuracy level exceeded for the first time
• System Characteristics
• 152 layer DL neural network
• Trained for approximately 3 weeks on 4 NVIDIA Tesla K80 GPUs using
a combined processing capability of 11.3 BFLOPs
CNN Deep Learning
Wikipedia https://en.wikipedi
a.org/wiki/Boltzmann_machine
• 2024, Prof. Geoffrey Hinton,
Nobel Prize in Physics
• Enabling machine learning with
artificial neural networks,
specifically for inventing the
Boltzmann machine
• Boltzmann machine: Unsupervised,
stochastic, and generative neural network that
learns to represent complex probability
distributions from input data using
symmetrically connected, bidirectional neurons
Times Colonist https://www.timescolonist.com/national-news/ai-pio
neer-geoffrey-hinton-receives-nobel-prize-in-physics-in-proud-mom
ent-for-u-of-t-9931604
Deep Learning
EEE2113 ECE101
Google TPUs & DeepMind
AlphaGo & AplhaFold
Jong-Moon Chung, Ph.D., IEEE Fellow, NAEK
Professor, School of Electrical & Electronic Engineering
Professor, Department of Emergency Medicine, College of Medicine
Associate Dean, College of Engineering, Yonsei University
Vice President of IEEE PSES, Director of IEEE CTSoc
Google AlphaGo
TPU (Tensor Processing Unit)
• Google’s ML (Machine Learning) ASIC (Application-Specific
Integrated Circuit) processor designed specifically for
TensorFlow
TensorFlow
• Open source ML (Machine Learning) software library
• Developed by the Google Brain Team
• Programmed in Python or C++
• Computation deployment to multiple TPUs, CPUs, GPUs
is possible
Google TensorFlow
TensorFlow
• Data flow graph based
numerical computation
• Nodes represent
mathematical operations
• Edges represent tensors
Adam (Adaptive Moment Estimation) Optimizer
• Gradient-based optimization algorithm used to train
deep learning systems
• Computes the adaptive learning rates for each parameter
• Accelerates convergence by combining AdaGrad (handling
sparse gradients) and RMSProp (dealing with
non-stationary objectives)
• Effective in solving large-scale, high-parameter, and
noisy machine learning problems
Data flow graph of
a Deep Learning
CNN (Convolutional
Neural Network) with
2 Convolution layers
with 2 full connection
Google TensorFlow
Tensor definition
• Geometric vector
• Describes geometric relations
1 5
3 47 8
2 9
• Organized multidimensional array of data values
• Used in Physics and Engineering in modeling
• Elasticity, fluid mechanics, general relativity, etc.
Google TensorFlow
TensorFlow was
the most popular
machine learning
language in GitHub
Tensorflow had earned
the most stars in GitHub
(June 2017)
Reference :
https://github.com/showcases/machinelearning?s=stars
Google TensorFlow
TensorFlow v0.5.0
• Released in November 9, 2015
• Initial release of Tensorflow
TensorFlow v0.6.0
• Released in December 2015
• Improvements to GPU performance and memory usage
• Performance improvements due to using
32-bit indices and faster shuffling kernels
• Support for Python 3.3+
Google TensorFlow
TensorFlow v0.7.0
• Released in February 2016
• Support for Nvidia CUDA (7.0 or higher) and cuDNN (R2 or higher)
• CUDA: Compute Unified Device Architecture
• cuDNN: CUDA Deep Neural Network library
• Support for contrib/ directory
• Enables processing of unsupported or experimental features, and higher level layers
modules
• Support for MetaGraphDef
• Enables easier saving of graphs with metadata
Google TensorFlow
TensorFlow v0.8.0
• Released in April 2016
• Support for TensorBoard displays of metadata stats
• Improved linear optimizer
• Added in contrib/linear_optimizer
• Support for adding a network file system
Google TensorFlow
TensorFlow v0.9.0
• Released in June 2016
• Support for Python 3.5 and iOS
• Support for GPU on Mac OS processing
• Support for makefile
• For better cross-platform build, C API only
Google TensorFlow
TensorFlow v0.10.0
• Released in July 2016
• Graph-construction C API added
• Support for C++ shape inference added
• Support for makefile build for iOS added
Google TensorFlow
TensorFlow v0.11.0
• Released in October 2016
• Support for HDFS added
• HDFS: Hadoop Distributed File System
• Support for Fused LSTM via cuDNN 5
• LSTM: Long Short-Term Memory (DL RNN)
• Added in tensorflow/contrib/cudnn_rnn
• Support for cuDNN 5 added
Google TensorFlow
TensorFlow v0.12.0
• Released in November 2016
• Support for Microsoft Windows
(Python, C++, CUDA 8.0, cuDNN 5.1)
• New library for matrix-free (iterative) solvers for linear equations,
linear least-squares, eigenvalues, and singular values added
• Added in tensorflow/contrib/solvers
• Solver for ordinary differential equations added
Google TensorFlow
TensorFlow v1.0.0
• Released in January 2017
• Added new Python 3 docker images
TensorFlow v1.1.0
― Released in March, 2017
― Support of Java APIs for Windows
― TensorFlow Spectral module (tf.spectral) added Includes 1D, 2D, and
3D real signal Fourier transform module
Google TensorFlow
TensorFlow v1.2.0
• Released in May 2017
• Support for Python 3.6 on Windows
• C library for Windows added
• Function to create dynamic TensorFlow clusters added
Google DeepMind AlphaGo
• Developed by Google DeepMind (London,
U.K.)
• AlphaGo is the first Go game program
to defeat professional human players
• Go is a Chinese board game played on
a 19x19 grid with White & Black
stones
• Considered the most challenging of classic
games due to (19x19)!=361! possibilities
Donarreiskoffer
https://commons.wikimedia.org
/wiki/File%3AGo_board.jpg
Google AlphaGo
• 2015 October
• AlphaGo wins all 5 games over the European Go champion
Fan, Hui
• “AlphaGo Fan” used
1,202 CPUs & 176 GPUs
• 2016 March
• AlphaGo wins 4:1 against
Lee, Sedol of South Korea
• “AlphaGo Lee” used 50
1st generation TPUs
Donga Science https://www.dongascience.com/news/10919
Google AlphaGo
• 2017 January
• AlphaGo won all 60 unofficial online matches over the world’s top
Go players
• “AlphaGo Master” used
one 2nd Generation TPU
• 2017 May
• AlphaGo wins all three games
against the world No.1 ranked
player Ke, Jie
Wired https://www.wired.com/2017/05/googles-alphago-levels-board-games-power-grids/
Google AlphaGo
TPU (Tensor Processing Unit)
• TPU 1st Generation
• Integer calculations
• 700 MHz clock speed
• TPU 2nd Generation
• Total performance 11.5 PFLOPS = 11.5 x 1015 FLOPS
• 256 chip pods
• 64 integrated 4-chip modules per chip pod
• 1-chip module is rated at 45 TFLOPS = 45 x 1012
Google AlphaGo
TPU 1G
Google Cloud https://cloud.google.com/blog/products/ai-machine-learnin
g/google-supercharges-machine-learning-tasks-with-custom-chip
TPU 2G
Wired
https://www.wired.com/2017/05/google-rattles-tech-world-new-ai-chip/
Google AlphaGo
TPU (Tensor Processing Unit)
• AlphaGo key technologies
• ATS (Advanced Tree Search)
• DNN (Deep Neural Network)
• AlphaGo Networks
• Policy Network
• DNN that selects the next move to play
• Value Network
• DNN that predicts the game winner
Google AlphaGo
Training Process
• Training based on ATS and DNN
• Initial training was based on Supervised Learning using
data from 160,000 games (30 million human moves)
• Advanced training (for AlphaGo Master)
was based on Reinforcement Learning
by playing games against itself
Google DeepMind AlphaFold
NobelPrize.org https://www.nobelprize.org/prizes/
chemistry/2024/hassabis/prize-presentation/
• 2024 Nobel Prize in Chemistry
for AlphaFold, Google’s DeepMind
CEO Demis Hassabis and researcher
John Jumper
• 2018, 1st place, 13th Critical
Assessment of Structure Prediction
• Predictions of protein structures
NobelPrize.org https://www.nobelprize.org/prizes/chem
istry/2024/popular-information/
Google TPU v7 Ironwood
TPU (Tensor Processing Unit)
―
Google's custom-developed accelerated AI processing chip that can provide higher efficiency
(performance-per-watt) compared to CPUs and GPUs.
―
Accelerated training & inference for TensorFlow, PyTorch, JAX, etc.
―
High-speed broadband ICIs (Inter-Chip Interconnects) are used to form TPU groups that are called
'Pods' which connect up to 9,216 chips to support large AI models
―
2015, Google started to use TPUs in 2015 only internally
―
2016, AlphaGo Lee uses TPU 1G
―
2027, AlphaGo Master uses TPU 2G
―
2018, Google made TPUs available for 3rd party use, chip for sale, and cloud accessible
―
2025 (latest version), TPU v7 (Ironwood) was released with the following specs
―
Memory: 192 GB HBM (High Bandwidth Memory), Memory bandwidth: 7.37 TB/s
―
Computational performance (trillion operations per second): 4614 fp8 (8-bit Floating Point)
―
Energy efficiency: 4.7 TOPS/W (Tera Operations per Second per Watt)
EEE2113 ECE101
Nvidia GPUs & CUDA
GeForce RTX 5090
RTX PRO 6000 Blackwell
Jong-Moon Chung, Ph.D., IEEE Fellow, NAEK
Professor, School of Electrical & Electronic Engineering
Professor, Department of Emergency Medicine, College of Medicine
Associate Dean, College of Engineering, Yonsei University
Vice President of IEEE PSES, Director of IEEE CTSoc
NVIDIA GPU GeForce RTX™ 5090
― GPU engineered for 4K/8K gaming with full ray tracing, DLSS 4, and advanced AI tasks
― DLSS (Deep Learning Super Sampling)
― AI-powered image enhancement suite used to boost the GeForce RTX GPUs (DLSS 4.5,
expected Q1 2026)
― Enhances the frame rates and image quality by improving the resolution and fidelity for games
(minimal performance impact)
― Deep Learning is used to upscale lower-resolution images, generate frames, and reconstruct
ray-traced scenes
― Price: Founders Edition base MSRP is $1,999. But currently (2026) due to high demand
and limited supply, the AIB (partner) card actual retail price is $3,000~$5,000
― Enhanced memory/bus and the 21,760 CUDA cores enable approximately 20-25% faster
than the previous-gen RTX 4090 at 4K resolution
NVIDIA GPU GeForce RTX™ 5090
― Memory: 32 GB GDDR7, Memory Bus: 512-bit with 1.79 TB/s bandwidth
― GDDR7 (Graphics Double Data Rate 7) HBM (High Bandwidth Memory) SDRAM (Synchro
nous Dynamic Random-Access Memory)
― Memory Integration (debuted with the GeForce RTX 50-series Blackwell architecture)
― Big increase in bandwidth for AI and high-end gaming support (uses PAM-3 signaling)
― Supports data rates 32 Gbps and higher using on-die ECC (Error Correcting Code) reli
ability
― Power supply requires (1000W+ PSU required) 16-pin connector and 575W power
draw
― Powered by the Blackwell architecture
NVIDIA GPU RTX PRO 6000 Blackwell
― NVIDIA Blackwell
―
2024, Nvidia's GPU platform (initially released in 2024)
―
GPU designed for AI, data centers, and workstation graphics
―
Delivers up to 2.5x the training performance and 5x higher AI inference performance
compared to the Hopper architecture
―
Built with TSMC 4nm process (4NP) with 208 billion transistors (evolving) on a dual-die chip
―
Blackwell Key models
―
GB200 Grace Blackwell Superchip
―
B200 GPU
―
RTX PRO 6000 Workstation Edition
― NVIDIA RTX PRO 6000 Blackwell
―
Blackwell architecture, 96 GB of GDDR7 memory, and 24,064 CUDA cores
―
Delivers 3511 AI TOPS
―
Available in workstation and server editions with a 600W power rating
Nvidia CUDA (Compute Unified Device Architecture)
― CUDA (Compute Unified Device Architecture)
―
Nvidia's proprietary parallel computing platform and application programming interface
(API)
2024 CUDA was created, 2027 officially released
2026 (12 January) stable release is version 13.1.1
CUDA Software Layer manages data, giving direct access to the GPU and CPU as necessary
CUDA API Library enable parallel computation for various needs
CUDA Drivers and Runtime Kernels help programmers accelerate their applications
CUDA is written in C, supports Windows & Linux OSs, and designed to work with C++,
Fortran, Python, Julia, etc.
―
―
―
―
―
―
― CUDA cores
―
―
―
―
Nvidia GPU’s parallel processing units
Designed for high-performance computing, AI, and graphics rendering
NVIDIA® GeForce RTX™ 5090 uses 21,760 CUDA cores
NVIDIA RTX PRO 6000 Blackwell uses 24,064 CUDA cores
AI (Artificial Intelligence) Technologies (selected)

Foundational AI = Fixed architecture
•

Evolutionary Algorithms, Genetic Algorithms, Fuzzy Logic, Game Theory
Machine Learning = Adaptive AI  AI system can learn (limited range)
•
Machine Learning Basic
-
•
SVM (Support Vector Machine), Linear Regression, Logistic Regression, Naive Bayes,
Decision tree, k-means, P-value, etc.
Deep Learning (Deep Neural Networks)  Enhanced AI learning & expanded memory
-
CNN (Convolutional Neural Networks), RNN (Recurrent Neural Networks)
-
Deep Reinforcement Learning (Deep Learning + Reinforcement Learning)
-
Generative AI
∙
GAN (Generative Adversarial Network), ARM (Autoregressive Model), Transformer (GPT)
•
Federated Learning (a.k.a., Collaborative Learning) & Transfer Learning
•
Agentic AI (a.k.a., AI Agent)
Advanced AI  AGI (Artificial General Intelligence)
•
DRL (Deep Reinforcement Learning)
•
•
•
GenAI (Generative AI)
•
VAE (Variational AutoEncoder), ARM (AutoRegressive Model),
•
FM (Flow Model), DM (Diffusion Model), GAN (Generative Adversarial Network), etc.
•
DALL-E, PARTI, GPT, GPT-2, GPT-3, ChatGPT, GPT-4, GPT-5.4, Claude OPUS 4.6, etc.
Agentic AI (AI Agent)
•
•
RAG (Retrieval Augmented Generation), ReAct (Reasoning and Acting), etc.
FL (Federated Learning), CL (Collaborative Learning), TL (Transfer Learning)
•
•
DQN (Deep Q-Network) and DDPG (Deep Deterministic Policy Gradient)
Centralized FL, Decentralized FL, Heterogeneous FL
DT (Digital Twin)
•
DTP (DT Prototype), DTI (DT Instance), DTA (DT Aggregate)