Machine Learning (AIL303m)
Vietnamese title: Học máy
Credits: 3
Prerequisites: MAS291, MAI391, PFP191

Description:
The course provides an introduction to Exploratory Data Analysis for Machine Learning (common techniques to retrieve your data, clean it, apply feature engineering, and have it ready for preliminary analysis and hypothesis testing), Supervised Machine Learning (how to train models to predict outcomes and how to use error metrics to compare across different supervised models), , Unsupervised Machine Learning (how to find insights from data sets that do not have a target or labeled variable. Learners will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data).

Learning outcomes:
Learners should be able to use common techniques to retrieve data from multiple data sources: SQL, NoSQL databases, APIs, Cloud
Implement a mini capstone ML project includes steps: data collection, data wrangling, exploratory data analysis, model development, model evaluation, and reporting
Learners should be able to describe and use common feature selection and feature engineering techniques
Learners should be able to use a variety of techniques for detecting and dealing with outliers
Learners should be able to describe and use supervised models
Learners should be able to use a variety of error metrics to compare and select a supervised model that best suits your data
Learners should be able to use oversampling and undersampling as techniques to handle unbalanced classes in a data
Learners should be able to explain the kinds of problems suitable for Unsupervised Learning approaches
Explain the curse of dimensionality, and how it makes clustering difficult with many features
Learners should be able to describe and use common clustering and dimensionality-reduction algorithms

Student tasks:
- Students must attend at least 80% of contact slots in order to be accepted to the final examination.
- Student is responsible to do all exercises, assignments and labs given by instructor in class or at home and submit on time
- Use laptop in class only for learning purpose
- Promptly access to the FU LMS at https://flm.fpt.edu.vn/ for up-to-date course information

Tools:
- jupyterlite
- JupyterLab

Materials:
description: https://www.coursera.org/specializations/ibm-intro-machine-learning
author: Coursera
publisher: Coursera
description: https://www.coursera.org/learn/ibm-exploratory-data-analysis-for-machine-learning?specialization=ibm-intro-machine-learning
author: Coursera
publisher: Coursera
description: https://www.coursera.org/learn/supervised-machine-learning-regression?specialization=ibm-intro-machine-learning
author: Coursera
publisher: Coursera
description: https://www.coursera.org/learn/supervised-machine-learning-classification?specialization=ibm-intro-machine-learning
author: Coursera
publisher: Coursera
description: https://www.coursera.org/learn/ibm-unsupervised-machine-learning?specialization=ibm-intro-machine-learning
author: Coursera
publisher: Coursera

Assessments:
category: Lab 1
type: on-going
weight: 10.0
duration: 1 week - 2 weeks
question_type: CLO1, CLO2, CLO3
knowledge_and_skill: 2
grading_guide: - cover content of Moduel 1
note: in home, by instructor
category: Lab 2
type: on-going
weight: 10.0
duration: 1 week - 2 weeks
question_type: CLO4, CLO5
knowledge_and_skill: 2
grading_guide: - cover content of Module 2
note: in home by instructor
category: Mini capstone
type: on-going
weight: 30.0
duration: 8 weeks
question_type: CLO10
knowledge_and_skill: 2
grading_guide: - cover content of all Modules
note: in home by instructor
category: Progress test 1
type: on-going
weight: 5.0
duration: 20'-30'
question_type: CLO1, CLO2, CLO3
no_question: Multiple choices
Marked by Computer or a suitable format
knowledge_and_skill: 20
grading_guide: - cover content of Module 1
note: in class, by instructor
category: Progress test 2
type: on-going
weight: 5.0
duration: 20'-30'
question_type: CLO4, CLO5
no_question: Multiple choices
Marked by Computer or a suitable format
knowledge_and_skill: 20
grading_guide: - cover content of Module 2
note: in class, by instructor
category: Progress test 3
type: on-going
weight: 5.0
duration: 20'-30'
question_type: CLO4, CLO5, CLO6
no_question: Multiple choices
Marked by Computer or a suitable format
knowledge_and_skill: 20
grading_guide: - cover content of Module 3
note: in class, by instructor
category: Progress test 4
type: on-going
weight: 5.0
duration: 20'-30'
question_type: CLO8, CLO9, CLO10
no_question: Multiple choices
Marked by Computer or a suitable format
knowledge_and_skill: 20
grading_guide: - cover content of Module 4
note: in class, by instructor
category: Final exam
type: Final exam
weight: 30.0
duration: 60'
question_type: CLO1, CLO2, CLO3, CLO4, CLO5, CLO6, CLO7, CLO8, CLO9
no_question: Multiple choices
Marked by Computer
knowledge_and_skill: 50
grading_guide: all studied modules
note: by exam board,
using computer

Schedule:
session: Course Introduction
1.1. A brief history of modern AI and its Application
1.1.1. Introduction to AI and Machine Learning
learning_outcomes: IT
materials: mat1
session: 1.1. A brief history of modern AI and its Application
1.1.2. Modern AI: Applications and Machine Learning Workflow
learning_outcomes: T
materials: FLM and Coursera
session: 1.2. Retrieving and Cleaning data
1.2.1. Retrieving data
1.2.2. Cleaning data
learning_outcomes: T
materials: FLM and Coursera
session: 1.3. Exploratory Data Analysis and Feature Engineering
1.3.1 Exploratory Data Analysis
learning_outcomes: T
materials: FLM and Coursera
session: 1.3. Exploratory Data Analysis and Feature Engineering
1.3.1 Exploratory Data Analysis
learning_outcomes: T
materials: FLM and Coursera
session: 1.3. Exploratory Data Analysis and Feature Engineering
1.3.2 Feature Engineering and Variable
Transformation
learning_outcomes: T
materials: FLM and Coursera
session: 1.3. Exploratory Data Analysis and Feature Engineering
1.3.2 Feature Engineering and Variable
Transformation
learning_outcomes: T
materials: FLM and Coursera
session: 1.4 Inferential Statistics and Hypothesis Testing
1.4.1 Estimation and Inference
learning_outcomes: T
materials: FLM and Coursera
session: 1.4 Inferential Statistics and Hypothesis Testing
1.4.2 Hypothesis Testing (Homework)
1.3.3 Demo Lab Feature Engineering
learning_outcomes: T
materials: FLM and Coursera
session: Progress test 1
Guiding mini Capstone
learning_outcomes: U
session: 2.1 Introduction to Supervised Machine Learning and Linear Regression
2.1.1 Introduction to Supervised Machine Learning
2.2 Data Splits and Cross Validation
2.2.1 Training and Test Splits
learning_outcomes: T
materials: FLM and Coursera
session: 2.2 Data Splits and Cross Validation
2.2.1 Training and Test Splits
learning_outcomes: T
materials: FLM and Coursera
session: 2.2 Data Splits and Cross Validation
2.2.2 Cross Validation
learning_outcomes: T
materials: FLM and Coursera
session: 2.3 Linear Regression
learning_outcomes: T
materials: FLM and Coursera
session: 2.4 Polynomial Regression
2.5 Regression with Regularization Techniques
2.5.1 Regularization Techniques
Elastic Net, Recursive Feature Elimination (Homework)
learning_outcomes: T
materials: FLM and Coursera
session: 2.5 Regression with Regularization Techniques
2.5.2 Polynomial Features and Regularization Demo
learning_outcomes: T
materials: FLM and Coursera
session: 2.5 Regression with Regularization Techniques
2.5.2 Polynomial Features and Regularization Demo
learning_outcomes: T
materials: FLM and Coursera
session: 2.5 Regression with Regularization Techniques
2.5.3. Details of Regularization
learning_outcomes: T
materials: FLM and Coursera
session: Progress Test 2
Review Lab 1
learning_outcomes: U
session: Review Lab 1
learning_outcomes: U
session: Review the mini Capstone
learning_outcomes: U
session: 3.1 Logistic Regression
3.1.1 Logistic Regression: Introduction to Classification and Error Metrics
learning_outcomes: T
materials: FLM and Coursera
session: 3.1 Logistic Regression
3.1.2 Error Metrics
3.1.3 Logistic Regression Labs
learning_outcomes: T
materials: FLM and Coursera
session: 3.1 Logistic Regression
3.1.2 Logistic Regression Labs
learning_outcomes: T
materials: FLM and Coursera
session: 3.2 K Nearest Neighbors
3.2.1 K Nearest Neighbors
learning_outcomes: T
materials: FLM and Coursera
session: 3.2 K Nearest Neighbors
3.2.1 K Nearest Neighbors
learning_outcomes: T
materials: FLM and Coursera
session: 3.2 K Nearest Neighbors
3.2.2 K Nearest Neighbors Labs
learning_outcomes: T
materials: FLM and Coursera
session: 3.2 K Nearest Neighbors
3.2.2 K Nearest Neighbors Labs
learning_outcomes: T
materials: FLM and Coursera
session: Review lab 2
learning_outcomes: U
session: Review the mini Capstone
learning_outcomes: U
materials: FLM and Coursera
session: 3.3 Support Vector Machines
3.3.1 Support Vector Machines
learning_outcomes: T
materials: FLM and Coursera
session: 3.3 Support Vector Machines
3.3.2 Support Vector Machines Kernels
learning_outcomes: T
materials: FLM and Coursera
session: 3.3 Support Vector Machines
3.3.3 Support Vector Machines Labs
learning_outcomes: T
materials: FLM and Coursera
session: 3.3 Support Vector Machines
3.3.3 Support Vector Machines Labs
learning_outcomes: T
materials: FLM and Coursera
session: 3.4 Decision Trees
3.4.1 Decision Trees
learning_outcomes: T
materials: FLM and Coursera
session: 3.4 Decision Trees
3.4.2 Decision Trees Labs
learning_outcomes: T
materials: FLM and Coursera
session: 3.5 Ensemble Models
3.5.1 Ensemble Based Methods and Bagging
learning_outcomes: T
materials: FLM and Coursera
session: 3.5 Ensemble Models
3.5.1 Ensemble Based Methods and Bagging
learning_outcomes: T
materials: FLM and Coursera
session: 3.5 Ensemble Models
3.5.2 Bagging Labs
learning_outcomes: T
materials: FLM and Coursera
session: 3.5 Ensemble Models
3.5.2 Bagging Labs
learning_outcomes: T
materials: FLM and Coursera
session: 3.5 Ensemble Models
3.5.3 Boosting and Stacking (Homework for Stacking)
learning_outcomes: T
materials: FLM and Coursera
session: 3.5 Ensemble Models
3.5.4 Boosting and Stacking Labs
learning_outcomes: T
materials: FLM and Coursera
session: 3.6 Modeling Unbalanced Classes
3.6.1 Model Interpretability (Homework)
3.6.2 Modeling Unbalanced Classes
learning_outcomes: T
materials: FLM and Coursera
session: 3.6 Modeling Unbalanced Classes
3.6.2 Modeling Unbalanced Classes
3.6.3 Practice Lab: Modeling Imbalanced Classes
learning_outcomes: T
session: Progress Test 3
Review the mini capstone
learning_outcomes: U
session: Review the mini capstone
learning_outcomes: U
session: 4.1 Introduction to Unsupervised Learning and K Means
4.1.1 Introduction to Unsupervised Learning
4.1.2 K Means Clustering
learning_outcomes: T
materials: FLM and Coursera
session: 4.1 Introduction to Unsupervised Learning and K Means
4.1.2 K Means Clustering
4.1.3 K-Means Lab
learning_outcomes: T
materials: FLM and Coursera
session: 4.2 Selecting a clustering algorithm
4.2.1 Distance Metrics of Clustering Algorithms
4.2.2 Common clustering algorithms
4.2.2.1 Hierarchical Agglomerative Clustering
learning_outcomes: T
materials: FLM and Coursera
session: 4.2 Selecting a clustering algorithm

4.2.2.2 DBSCAN (homework)
4.2.2.3 Mean-shift (howework)
4.2.2.4Hierarchical Agglomerative Clustering Lab
learning_outcomes: T
materials: FLM and Coursera
session: 4.2 Selecting a clustering algorithm
4.2.3 Compare clustering algorithms
learning_outcomes: T
materials: FLM and Coursera
session: 4.2 Selecting a clustering algorithm
4.2.3 Compare clustering algorithms
Clustering Demo
learning_outcomes: T
materials: FLM and Coursera
session: 4.3 Dimensionality Reduction
4.3.1 Dimensionality Reduction
learning_outcomes: T
materials: FLM and Coursera
session: 4.3 Dimensionality Reduction
4.3.2 Principal Component Analysis
learning_outcomes: T
materials: FLM and Coursera
session: 4.3 Dimensionality Reduction
4.3.2 Matrix Factorization (homework)
4.3.3 Principal Component Analysis Lab
learning_outcomes: T
session: Progress test 4
Review mini Capstone
learning_outcomes: U
session: Review the mini Capstone
learning_outcomes: U
session: Review the mini Capstone
learning_outcomes: U
session: Review
learning_outcomes: U
materials: FLM and Coursera
session: Review
learning_outcomes: U
materials: FLM and Coursera