This course aims to cultivate theoretical knowledge and hands-on experience necessary for students from various backgrounds to deal with and analyze big data. Through this course, students learn the basic knowledge of data-oriented computing, quantitative thinking and reasoning, and exploratory data analysis. Based on this, students learn key principles and techniques for data-driven problem solving, such as data analysis methods, big data management systems, problem formulation, data collection and organization, visualization, reasoning, predictive modeling, and decision making.
Date | Topic | Instructor | Due |
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Introduction · Course Logistics· What is Data Science? · Lab: Linux, Python Programming | Joonseok Lee Sang Kyun Cha Jaejin Lee | ||
Statistics for Data Science, Basic Programming · Data sampling, Probability· Lab: Python Programming | Seunggeun Lee Jaejin Lee | ||
Statistics for Data Science, Basic Programming · Random variables and Expectation· Lab: Python Programming | Seunggeun Lee Jaejin Lee | ||
Statistics for Data Science, Basic Programming · Variance and Asymptotics· Lab: Python Programming | Seunggeun Lee Jaejin Lee | ||
Statistics for Data Science, Basic Programming · Estimation, Bias and Mean squared error· Lab: Python Programming | Seunggeun Lee Jaejin Lee |
Date | Topic | Instructor | Due |
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Algorithmic Thinking, Computational Complexity · Time and Space Complexity· Lab: Peak Finding | Min-hwan Oh | ||
Searching · Searching (Binary search)· Lab: Searching Problems | Min-hwan Oh | ||
Sorting · Sorting (Insertion sort, Selection sort, Merge sort, Quick sort)· Lab: Python Programming | Min-hwan Oh | ||
Data Structures · Array, Linked list· Lab: Array, Linked list Problems | Hyung-Sin Kim | ||
Data Structures · Stack, Queue· Lab: Stack, Queue Problems | Hyung-Sin Kim | ||
Data Structures · Trees (Binary tree, Binary search tree)· Lab: Trees Problems | Hyung-Sin Kim | ||
Data Structures · Graph, Hash table· Lab: Graph, Hash table Problems | Hyung-Sin Kim |
Date | Topic | Instructor | Due |
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Introduction to Database · Introduction to Database· Lab: SQL | Sang Kyun Cha | ||
Graph Database · Graph Database· Lab: Neo4j | Sang Kyun Cha | ||
Mid-term Exam | Last day to unregister |
Date | Topic | Instructor | Due |
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Linear Regression · Introduction to ML, Linear Regression· Lab: Linear Regression | Sanghack Lee | ||
Linear Regression · Logistic Regression· Lab: Logistic Regression | Sanghack Lee | ||
Decision Trees · Decision Trees· Lab: Decision Trees, Random forests | Sanghack Lee | ||
Overfitting, Regularization · Overfitting, Regularizaion· Lab: Regularization Methods | Sanghack Lee | ||
Nearest Neighbors · Nearest Neighbor Classifiers· Lab: Handwritten Digits Classification using Nearest Neighbors | Joonseok Lee | ||
Optimization · Gradient descent, SGD, advanced optimization, Cross validation· Lab: Gradient descent | Joonseok Lee | ||
Neural Networks · Neural networks, Backpropagation· Lab: Neural networks with TensorFlow | Joonseok Lee | ||
Introduction to Deep Learning · Deep learning, Convolutional neural networks (CNN)· Lab: CNN-based Image Classification | Joonseok Lee | ||
Unsupervised Learning · Clustering, Dimension reduction· Lab: K-means Clustering for Image Compression | Joonseok Lee |
Date | Topic | Instructor | Due |
---|---|---|---|
Decision Making · Reinforcement learning· Lab: Reinforcement learning | Min-hwan Oh | ||
Ambient AI · Ambient AI· Lab: Ambient AI with Edge Devices | Hyung-Sin Kim | ||
Causal Inference · Causal Inference· Lab: Causal Inference | Sanghack Lee | ||
Final Exam |