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노트(Notes)
도움이 될 수 있는 정보(Potentially Useful Info.)
맛집(Good Restaurants)
문화생활
여행(Trip, Travel)
건강(Health)
주제별 노트(Notes by Subjects)
언어(Language)
수학(Mathematics)
과학(Science)
컴퓨터(Computer)
금융 공학(Financial Engineering)
R을 활용한 퀀트 전략 구현(Quant Strategy Implementation Using R)
의학(Medical Science)
영양과 건강(Nutrition & Health)
인공지능(Artificial Intelligence, AI)
Artificial Intelligence
1. Introduction to Artificial Intelligence
1-1. Welcome to Artificial Intelligence
1-2. Getting Help
1-3. Get Help with Your Account
1-4. Intro to Artificial Intelligence
1-5. Solving Sudoku With AI
1-6. Workspaces
1-7. Setting Up Your Environment with Anaconda
1-8. Build a Sudoku Solver
1-9. Jobs in AI
2. Constraint Satisfaction Problems
2-1. Constraint Satisfaction Problems
2-2. CSP Coding Exercise
2-3. Additional Readings
3. Classical Search
3-1. Introduction
3-2. Uninformed Search
3-3. Informed Search
3-4. Classroom Exercise: Search
3-5. Additional Search Topics
4. Automated Planning
4-1. Symbolic Logic & Reasoning
4-2. Introduction to Planning
4-3. Classical Planning
4-4. PROJECT: Build a Forward-Planning Agent
4-5. Additional Planning Topics
5. (Optional) Optimization Problems
5-1. Introduction
5-2. Hill Climbing
5-3. Simulated Annealing
5-4. Genetic Algorithms
5-5. Optimization Exercise
5-6. Additional Optimization Topics
6. Adversarial Search
6-1. Search in Multiagent Domains
6-2. Optimizing Minimax Search
6-3. PROJECT: Build an Adversarial Game Playing Agent
6-4. Extending Minimax Search
6-5. Additional Adversarial Search Topics
7. Probabilistic Models
7-1. Probability
7-2. Naive Bayes
7-3. Bayes Nets
7-4. Inference in Bayes Nets
7-5. Hidden Markov Models
7-6. PROJECT: Part of Speech Tagging
7-7. Dynamic Time Warping
7-8. Additional Topics in PGMs
8. After the AI Nanodegree Program
8-1. Additional Topics in AI
9. Extracurricular
9-1. Extracurricular
9-1-1. Hidden Markov Models
9-1-2. Advanced HMMs
9-2. Career Services
9-2-1. PROJECT: Take 30 Min to Improve your LinkedIn
9-2-2. PROJECT: Optimize Your GitHub Profile
Artificial Intelligence for Trading
1. Quantitative Trading
1-1. Welcome to the Nanodegree Program
1-2. Getting Help
1-3. Get Help with Your Account
1-4. Stock Prices
1-5. Market Mechanics
1-6. Data Processing
1-7. Stock Returns
1-8. Momentum Trading
1-9. Project 1: Trading with Momentum
1-10. Quant Workflow
1-11. Outliers and Filtering
1-12. Regression
1-13. Time Series Modeling
1-14. Volatility
1-15. Pairs Trading and Mean Reversion
1-16. Project 2: Breakout Strategy
1-17. Stocks, Indices, Funds
1-18. ETFs
1-19. Portfolio Risk and Return
1-20. Portfolio Optimization
1-21. Project 3: Smart Beta and Portfolio Optimization
1-22. Factors
1-23. Factor Models and Types of Factors
1-24. Risk Factor Models
1-25. Time Series and Cross Sectional Risk Models
1-26. Risk Factor Models with PCA
1-27. Alpha Factors
1-28. Alpha Factor Research Methods
1-29. Advanced Portfolio Optimization
1-30. Project 4: Alpha Research and Factor Modeling
2. AI Algorithms in Trading
2-1. Welcome To Term II
2-2. Intro to Natural Language Processing
2-3. Text Processing
2-4. Feature Extraction
2-5. Financial Statements
2-6. Basic NLP Analysis
2-7. Project 5: NLP on Financial Statements
2-8. Introduction to Neural Networks
2-9. Training Neural Networks
2-10. Deep Learning with PyTorch
2-11. Recurrent Neural Networks
2-12. Embeddings & Word2Vec
2-13. Sentiment Prediction RNN
2-14. Project 6: Sentiment Analysis with Neural Networks
2-15. Overview
2-16. Decision Trees
2-17. Model Testing and Evaluation
2-18. Random Forests
2-19. Feature Engineering
2-20. Overlapping Labels
2-21. Feature Importance
2-22. Project 7: Combining Signals for Enhanced Alpha
2-23. Intro to Backtesting
2-24. Optimization with Transaction Costs
2-25. Attribution
2-26. Project 8: Backtesting
3. Extracurricular
3-1. Python Refresher
3-1-1. Why Python Programming
3-1-2. Data Types and Operators
3-1-3. Control Flow
3-1-4. Functions
3-1-5. Scripting
3-2. Linear Algebra
3-2-1. Introduction
3-2-2. Vectors
3-2-3. Linear Combination
3-2-4. Linear Transformation and Matrices
3-3. Jupyter Notebook, Numpy, and Pandas
3-3-1. Jupyter Notebooks
3-3-2. NumPy
3-3-3. Pandas
3-4. Statistics
3-4-1. Descriptive Statistics – Part I
3-4-2. Descriptive Statistics – Part II
3-4-3. Admissions Case Study
3-4-4. Probability
3-4-5. Binomial Distribution
3-4-6. Conditional Probability
3-4-7. Bayes Rule
3-4-8. Python Probability Practice
3-4-9. Normal Distribution Theory
3-4-10. Sampling distributions and the Central Limit Theorem
3-4-11. Confidence Intervals
3-4-12. Hypothesis Testing
3-4-13. Case Study: A/B tests
3-5. Machine Learning
3-5-1. Linear Regression
3-5-2. Naive Bayes
3-5-3. Clustering
3-5-4. Decision Trees
3-5-5. Introduction to Kalman Filters
3-6. Deep Learning
3-6-1. Introduction to Neural Networks
3-7. Computer Vision
3-7-1. Intro to Computer Vision
3-8. Natural Language Processing
3-8-1. Intro to NLP
3-9. Career Services
3-9-1. PROJECT: Take 30 Min to Improve your LinkedIn
3-9-2. PROJECT: Optimize Your GitHub Profile
Computer Vision
1. Introduction to Computer Vision
1-1. Welcome to Computer Vision
1-2. Getting Help
1-3. Get Help with Your Account
1-4. Image Representation & Classification
1-5. Convolutional Filters and Edge Detection
1-6. Types of Features & Image Segmentation
1-7. Feature Vectors
1-8. CNN Layers and Feature Visualization
1-9. Project: Facial Keypoint Detection
1-10. Jobs in Computer Vision
2. Optional: Cloud Computing
2-1. Cloud Computing with Google Cloud
2-2. Optional: Cloud Computing with AWS
3. Advanced Computer Vision & Deep Learning
3-1. Advanced CNN Architectures
3-2. YOLO
3-3. RNN’s
3-4. Long Short-Term Memory Networks (LSTMs)
3-5. Hyperparameters
3-6. Optional: Attention Mechanisms
3-7. Image Captioning
3-8. Project: Image Captioning
3-9. Optional: Cloud Computing with AWS
4. Object Tracking and Localization
4-1. Introduction to Motion
4-2. Robot Localization
4-3. Mini-project: 2D Histogram Filter
4-4. Introduction to Kalman Filters
4-5. Representing State and Motion
4-6. Matrices and Transformation of State
4-7. Simultaneous Localization and Mapping
4-8. Optional: Vehicle Motion and Calculus
4-9. Project: Landmark Detection & Tracking (SLAM)
5. Extracurricular
5-1. Applications of Computer Vision and Deep Learning
5-1-1. Applying Deep Learning Models
5-2. Review: Training A Neural Network
5-2-1. Feedforward and Backpropagation
5-2-2. Training Neural Networks
5-2-3. Deep Learning with PyTorch
5-3. Skin Cancer Detection
5-3-1. Deep Learning for Cancer Detection with Sebastian Thrun
5-4. Text Sentiment Analysis
5-4-1. Sentiment Analysis
5-5. More Deep Learning Models
5-5-1. Fully-Convolutional Neural Networks & Semantic Segmentation
5-6. C++ Programming
5-6-1. C++ Getting Started
5-6-2. C++ Vectors
5-6-3. Practical C++
5-6-4. C++ Object Oriented Programming
5-6-5. Python and C++ Speed
5-6-6. C++ Intro to Optimization
5-6-7. C++ Optimization Practice
5-6-8. Project: Optimize Histogram Filter
5-7. Career Services
5-7-1. PROJECT: Take 30 Min to Improve your LinkedIn
5-7-2. PROJECT: Optimize Your GitHub Profile
Deep Reinforcement Learning
1. Introduction to Deep Reinforcement Learning
1-1. Welcome to Deep Reinforcement Learning
1-2. Getting Help
1-3. Get Help with Your Account
1-4. Learning Plan
1-5. Introduction to RL
1-6. The RL Framework: The Problem
1-7. The RL Framework: The Solution
1-8. Monte Carlo Methods
1-9. Temporal-Difference Methods
1-10. Solve OpenAI Gym’s Taxi-v2 Task
1-11. RL in Continuous Spaces
1-12. What’s Next?
2. Value-Based Methods
2-1. Study Plan
2-2. Deep Q-Networks
2-3. PROJECT: Navigation
2-4. Opportunities in Deep Reinforcement Learning
3. Policy-Based Methods
3-1. Study Plan
3-2. Introduction to Policy-Based Methods
3-3. Policy Gradient Methods
3-4. Proximal Policy Optimization
3-5. Actor-Critic Methods
3-6. Deep RL for Finance (Optional)
3-7. PROJECT: Continuous Control
4. Multi-Agent Reinforcement Learning
4-1. Study Plan
4-2. Introduction to Multi-Agent RL
4-3. Case Study: AlphaZero
4-4. PROJECT: Collaboration and Competition
5. Extracurricular
5-1. Special Topics in Deep Reinforcement Learning
5-1-1. Dynamic Programming
5-2. Neural Networks in PyTorch
5-2-1. Neural Networks
5-2-2. Convolutional Neural Networks
5-2-3. Deep Learning with PyTorch
5-3. Computing Resources
5-3-1. Udacity Workspaces
5-4. C++ Programming
5-4-1. C++ Getting Started
5-4-2. C++ Vectors
5-4-3. Practical C++
5-4-4. C++ Object Oriented Programming
5-4-5. C++ Intro to Optimization
5-4-6. C++ Optimization Practice
5-5. Career Services
5-5-1. PROJECT: Take 30 Min to Improve your LinkedIn
5-5-2. PROJECT: Optimize Your GitHub Profile
Natural Language Processing
1. Introduction to Natural Language Processing
1-1. Welcome to Natural Language Processing
1-2. Getting Help
1-3. Get Help with Your Account
1-4. Intro to NLP
1-5. Text Processing
1-6. Spam Classifier with Naive Bayes
1-7. Part of Speech Tagging with HMMs
1-8. Project: Part of Speech Tagging
1-9. (Optional) IBM Watson Bookworm Lab
1-10. Jobs in NLP
2. Computing with Natural Language
2-1. Feature extraction and embeddings
2-2. Topic Modeling
2-3. Sentiment Analysis
2-4. Sequence to Sequence
2-5. Deep Learning Attention
2-6. RNN Keras Lab
2-7. Project: Machine Translation
3. Communicating with Natural Language
3-1. Intro to Voice User Interfaces
3-2. (Optional) Alexa History Skill
3-3. Speech Recognition
3-4. Project: DNN Speech Recognizer
4. Extracurricular
4-1. Recurrent Neural Networks
4-1-1. Recurrent Neural Networks
4-1-2. Long Short-Term Memory Networks (LSTM)
4-1-3. Hyperparameters
4-2. Keras
4-2-1. Keras
4-3. Sentiment Analysis Extras
4-3-1. Sentiment Analysis with Andrew Trask
4-3-2. Sentiment Prediction RNN
4-4. TensorFlow
4-4-1. TensorFlow
4-5. Embeddings and Word2Vec
4-5-1. Embeddings and Word2Vec
4-6. PyTorch
4-6-1. Introduction to PyTorch
4-6-2. Embeddings & Word2Vec
4-6-3. Implementation of RNN & LSTM
4-6-4. Deploying PyTorch Models
4-7. Additional Text Preprocessing
4-7-1. Python Regular Expression
4-7-2. BeautifulSoup Library
4-8. Career Services
4-8-1. PROJECT: Take 30 Min to Improve your LinkedIn
4-8-2. PROJECT: Optimize Your GitHub Profile
시험 정보(Tests Info.)
수능(Korean CSAT)
국어(Korean)
수학(Math)
영어(English)
한국사(Korean History)
탐구(Inquiry)
사회, 과학(Social Studies and Science)
사회(Social Studies)
국사(Korean History (from prehistory to early modern history))
한국 근·현대사(Korean Modern and Contemporary History)
동아시아사(East Asia History)
세계사(World History)
경제(Economics)
경제 지리(Economic Geography)
한국 지리(Korean Geography)
윤리(Ethics)
윤리와 사상(Ethics and Thoughts)
생활과 윤리(Life and Ethics)
사회·문화(Society and Culture)
세계 지리(World Geography)
정치(Politics)
법과 사회(Law and Society)
정치와 법, 법과 정치(Laws, Government and Politics)
과학(Science)
물리학Ⅰ, 물리Ⅰ(Physics Ⅰ)
물리학Ⅱ, 물리Ⅱ(Physics Ⅱ)
화학Ⅰ(Chemistry Ⅰ)
화학Ⅱ(Chemistry Ⅱ)
생물학Ⅰ, 생명 과학Ⅰ(Biology Ⅰ)
생물학Ⅱ, 생명 과학Ⅱ(Biology Ⅱ)
지구 과학Ⅰ(Earth Science Ⅰ)
지구 과학Ⅱ(Earth Science Ⅱ)
직업(Vocational Education)
인간 발달(Human Development)
식품과 영양(Food and Nutrition)
가사·실업1(Home EconomicsㆍVocational Education Ⅰ)
가사·실업2(Home EconomicsㆍVocational Education Ⅱ)
생활 서비스 산업의 이해(Understanding of Living Service Industry)
회계 원리(Principles of Accounting)
상업 경제(Commercial Economics)
상업 정보1(Commercial Information Ⅰ)
상업 정보2(Commercial Information Ⅱ)
농업 이해(Understanding of Agriculture)
농업 기초 기술(Basic Agriculture)
농생명 산업1(Agriculture and Biotechnology Industry Ⅰ)
농생명 산업2(Agriculture and Biotechnology Industry Ⅱ)
농업 정보 관리(Agricultural Information Management)
디자인 일반(General Design)
기초 제도(Basic Drafting)
공업 입문(Introduction to Industry)
공업 일반(General Industry)
공업 Ⅰ(Industry Ⅰ)
공업 Ⅱ(Industry Ⅱ)
수산 일반(Introduction to Fisheries)
수산·해운 산업 기초(Basic FisheryㆍShipping Industry)
수산·해운1(FisheryㆍMaritime Transformation Ⅰ)
수산·해운2(FisheryㆍMaritime Transformation Ⅱ)
수산·해운 정보 처리(FisheryㆍShipping Information Processing)
해양의 이해(Understanding of Marine)
해양 일반(General Oceanography)
해사 일반(Maritime)
정보 기술 기초(Basic Information Technology)
컴퓨터 일반(General Computers)
프로그래밍(Programming)
제2외국어(Second Foreign Language)
독일어Ⅰ(German Ⅰ)
프랑스어Ⅰ(French Ⅰ)
에스파냐어Ⅰ, 스페인어Ⅰ(Spanish Ⅰ)
중국어Ⅰ(Chinese Ⅰ)
일본어Ⅰ(Japanese Ⅰ)
러시아어Ⅰ(Russian Ⅰ)
아랍어Ⅰ(Arabic Ⅰ)
기초 베트남어(Basic Vietnamese)
베트남어Ⅰ(Vietnamese Ⅰ)
한문(Chinese Characters and Classics)
한문Ⅰ(Chinese Characters and Classics Ⅰ)
와인(Wines)
3-1-2-28. Quiz: Tuples
2021-08-05
by
Dr. Serendipity
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