Samsung Innovation Campus

Pre Test

Unit 1: The Concept of Artificial Intelligence

1.1 Definition of Artificial Intelligence

1.2 Types and Subsets of Artificial Intelligence

1.3 Definition of Machine Learning

1.4 Disciplines Related to Machine Learning

1.5 Types and Choices of Machine Learning-based Data Analysis

1.6 Procedures for Machine Learning-based Data Analysis

1.7 Reasons For Machine Learning

1.8 Limitations of Machine Learning

Unit 2: Applications of AI

2.1 Applications of Artificial Intelligence

2.2 Image Recognition

2.3 Computer Vision & Machine Vision

2.4 Speech Intelligence

Unit 3: Techniques in Artificial Intelligence

3.1 Edge AI

3.2 Medical Imaging & Diagnostics

3.3 Autonomous Vehicle

3.4 Reinforcement Learning

3.5 Conversational AI

3.6 GAN, XAI, Synthetic Training Data

Unit 4: Artificial Intelligence: Trends and Markets

4.1 AI Trends

4.2 AI Markets

4.3 AI in Sustainable Energy

4.4 AI in Financial Services

4.5 AI in Government

4.6 AI in Healthcare

4.7 IoT and AI in Agriculture

Unit 5: Course Roadmap

5.1 Artificial Intelligence Course Roadmap

5.2 Category of Machine Learning Techniques

Introduction to AI (Summary)

Chapter 1: Introduction to Artificial Intelligence

Unit 1: Introduction

1.1 Installing Anaconda for Python

1.2 Intro to Mathematics

1.3 Mathematical Symbols

Unit 2: Basic Math for Data Science

2.1 Algebra

2.2 Sequence

2.3 Absolute Value and Euclidean Distance

2.4 Sets

2.5 Concept of Functions

2.6 Exponential and Logarithmic Functions

2.7 Natural Logarithms

2.8 Sigmoid Functions

2.9 Trigonometric Functions

Unit 3: Understanding Data Science: Vector

3.1 Vector

3.2 Vector Norm

3.3 Inner Product

3.4 Orthogonal Condition

3.5 Normal Vector

Cosine Similarity

Unit 4: Understanding Data Science: Matrix

4.1 Calculating Matrix

4.2 Reverse Matrix

4.3 Linear Transformation

4.4 Eigenvalues and Eigenvectors

Unit 5: Understanding Deep Learning: Derivatives

5.1 Limits

5.2 Differential Coefficient and Derivatives

5.3 Differential Method

5.4 Difference Between Logarithmic and Exponential

5.5 Derivatives of Composite Functions

5.6 High Order Derivatives and Partial Derivatives

5.7 Derivative of the Sigmoid Function

Chapter 2: Math for Data Science (Slides)

Chapter 2: Quiz

Live Session for Chapter 2

Unit 1: NumPy Array Data Structure for Optimal Computational Performance

1.1. NumPy Arrays

1.2. NumPy Array Basics

1.3. NumPy Array Operations

1.4. NumPy Indexing and Slicing

1.5. Array Transposition and Axis Swap

Unit 2: Optimal Data Exploration Through Pandas

2.1. Pipelines: Data Structures According to Data Types

2.2.1. Pandas Series and DataFrames I

2.2.2. Pandas Series and DataFrames II

2.2.3. Pandas Series and DataFrames III

2.3. Merging and Binding DataFrames

2.4. DataFrame Sorting and Multi-Index

2.5. Examining the Characteristics of Data Through Descriptive Statistics and Data Samples

Unit 3: Pandas Data Preprocessing for Optimal Model Execution

3.1. Data Preprocessing

3.2. Identifying Data Properties

3.3. Checking for Missing Data

3.4. Checking and Processing Duplicate Data

3.5. Data Feature Engineering

Unit 4: Data Visualization for Various Data Scales

4.1. Intro to Data Visualization

4.2. Graphs for Continuous Data Summary

4.3. Graphs for Categorical Data Summary

4.4. Visualization for Matplotlib & Pandas

4.5. Advanced Graphing with Seaborn

Chapter 3: Exploratory Data Analysis

Live Session for Chapter 3

Chapter 3: Quiz

Introduction

Unit 1: Understanding of Probability

1.1 Probability Theory

1.2 Probability Rules

1.3 Random Variable

1.4 Discrete Probability Distribution

Unit 2: Understanding of Statistics I

2.1 Continuous Probability Density

2.2 Conjoint Probability

Unit 3: Understanding of Statistics II

3.1 Descriptive Statistics

3.2 Central Limit Theorem

3.3 Estimation Theory

Unit 4: Statistical Hypothesis and Testing

4.1 Principles of Hypothesis Testing

4.2 Hypothesis Testing in Action

Chapter 4: Probability & Statistics

Chapter 4 Quiz

Live session for Chapter 4

Introduction

Unit 1: Machine Learning Based Data Analysis

1.1 What is Machine Learning?

1.2 Python scikit-learn Library for Machine Learning

1.3 Preparation and Division of Dataset

1.4 Data Pre-processing for Making a Good Training Dataset

1.5 Practicing to Find an Optimal Method to Solve Problems with scikit-learn

Unit 2: Application of Supervised Learning Model for Numerical Prediction

2.1 Training and Testing in Machine Learning

2.2 Linear Regression Basics

2.3 Linear Regression Diagnostics

2.4 Other Regression Types

2.5 Practicing the Supervised Learning Model for Numerical Prediction

Unit 3: Application of Supervised Learning Model for Classification

3.1 Training and Testing in Machine Learning

3.2 Logistic Regression Basics

3.3 Logistic Regression Performance Metrics

Unit 4: Decision Tree

4.1 Tree Algorithm

Unit 5: Naïve Bayes Algorithm

5.1 Naïve Bayes Algorithm

Unit 6: KNN Algorithm

6.1 KNN Algorithm

Unit 7: SVM Algorithm

7.1 SVM Algorithm

Unit 8: Ensemble Algorithm

8.1 The Concept of Ensemble Algorithm and Voting

8.2 Bagging & Random Forest

8.3 Boosting

Chapter 5 Quiz

Machine Learning 1 - Supervised Learning

Live Session for Chapter 5