[major=AI | course_id=AIL303m | syllabus_id=10736 | semester=4 | combo=core | title=Machine Learning]
Course ID: AIL303m
Title EN: Machine Learning
Title VI: Học máy
Credits: 3.0
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).
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
Course type: coursera_hybrid
Decision: 358/QĐ-ĐHFPT dated 04/03/2024
Approved date: 2024-04-03
CLO1: Learners should be able to use common techniques to retrieve data from multiple data sources: SQL, NoSQL databases, APIs, Cloud
[1] category=Lab 1; type=on-going; weight=10.0; duration=1 week - 2 weeks; question_type=CLO1, CLO2, CLO3; grading_guide=- cover content of Moduel 1; completion_criteria=; clos=>0
[2] category=Lab 2; type=on-going; weight=10.0; duration=1 week - 2 weeks; question_type=CLO4, CLO5; grading_guide=- cover content of Module 2; completion_criteria=; clos=>0
[3] category=Mini capstone; type=on-going; weight=30.0; duration=8 weeks; question_type=CLO10; grading_guide=- cover content of all Modules; completion_criteria=; clos=>0
[4] category=Progress test 1; type=on-going; weight=5.0; duration=20'-30'; question_type=CLO1, CLO2, CLO3; grading_guide=- cover content of Module 1; completion_criteria=; clos=>0
[5] category=Progress test 2; type=on-going; weight=5.0; duration=20'-30'; question_type=CLO4, CLO5; grading_guide=- cover content of Module 2; completion_criteria=; clos=>0
[6] category=Progress test 3; type=on-going; weight=5.0; duration=20'-30'; question_type=CLO4, CLO5, CLO6; grading_guide=- cover content of Module 3; completion_criteria=; clos=>0
[7] category=Progress test 4; type=on-going; weight=5.0; duration=20'-30'; question_type=CLO8, CLO9, CLO10; grading_guide=- cover content of Module 4; completion_criteria=; clos=>0
[8] category=Final exam; type=Final exam; weight=30.0; duration=60'; question_type=CLO1, CLO2, CLO3, CLO4, CLO5, CLO6, CLO7, CLO8, CLO9; grading_guide=all studied modules; completion_criteria=; clos=4
[1] desc=https://www.coursera.org/specializations/ibm-intro-machine-learning; author=Coursera; publisher=Coursera; edition=; isbn=; main=True; online=True; note=
[2] desc=https://www.coursera.org/learn/ibm-exploratory-data-analysis-for-machine-learning?specialization=ibm-intro-machine-learning; author=Coursera; publisher=Coursera; edition=; isbn=; main=True; online=True; note=
[3] desc=https://www.coursera.org/learn/supervised-machine-learning-regression?specialization=ibm-intro-machine-learning; author=Coursera; publisher=Coursera; edition=; isbn=; main=True; online=True; note=
[4] desc=https://www.coursera.org/learn/supervised-machine-learning-classification?specialization=ibm-intro-machine-learning; author=Coursera; publisher=Coursera; edition=; isbn=; main=True; online=True; note=
[5] desc=https://www.coursera.org/learn/ibm-unsupervised-machine-learning?specialization=ibm-intro-machine-learning; author=Coursera; publisher=Coursera; edition=; isbn=; main=True; online=True; note=
Session 1: Course Introduction
1.1. A brief history of modern AI and its Application
1.1.1. Introduction to AI and Machine Learning
Topic: Offline; Teaching type: CLO1; LO: IT
ITU: Slide , Videos of Introduction to Artificial Intelligence and Machine Learning
Materials: mat1
Download: Read Slide and Watch Video

Session 2: 1.1. A brief history of modern AI and its Application
1.1.2. Modern AI: Applications and Machine Learning Workflow
Topic: Offline; Teaching type: CLO1; LO: T
ITU: Slide, Videos of Modern AI: Applications and the Machine Learning Workflow
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 3: 1.2. Retrieving and Cleaning data
1.2.1. Retrieving data
1.2.2. Cleaning data
Topic: Offline; Teaching type: CLO1; LO: T
ITU: Slide, Videos of Retrieving Data and Cleaning Data
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 4: 1.3. Exploratory Data Analysis and Feature Engineering
1.3.1 Exploratory Data Analysis
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Exploratory Data Analysis
Materials: FLM and Coursera
Download: Read Slide and Watch Video
CLO10: Implement a mini capstone ML project includes steps: data collection, data wrangling, exploratory data analysis, model development, model evaluation, and reporting
Session 1: 1.3. Exploratory Data Analysis and Feature Engineering
1.3.1 Exploratory Data Analysis
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Exploratory Data Analysis
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 2: 1.3. Exploratory Data Analysis and Feature Engineering
1.3.2 Feature Engineering and Variable
Transformation
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Feature Engineering and Variable
Transformation
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 3: 1.3. Exploratory Data Analysis and Feature Engineering
1.3.2 Feature Engineering and Variable
Transformation
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Feature Engineering and Variable
Transformation
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 4: 1.4 Inferential Statistics and Hypothesis Testing
1.4.1 Estimation and Inference
Topic: Offline; Teaching type: CLO3; LO: T
ITU: Slide, Videos of Estimation and Inference, and Hypothesis Testing
Materials: FLM and Coursera
Download: Read Slide and Watch Video
CLO2: Learners should be able to describe and use common feature selection and feature engineering techniques
Session 1: 1.4 Inferential Statistics and Hypothesis Testing
1.4.2 Hypothesis Testing (Homework)
1.3.3 Demo Lab Feature Engineering
Topic: Offline; Teaching type: CLO3; LO: T
ITU: Slide, Videos of Hypothesis Testing
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 2: Progress test 1
Guiding mini Capstone
Topic: Offline; Teaching type: CLO10; LO: U
ITU: - Content 1 - 9.
- Progress test 1.
- The mini capstone (.docx file)
Download: - Review Content 1 - 9
- Do Progress test 1

Session 3: 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
Topic: Offline; Teaching type: CLO4; LO: T
ITU: Slide, Videos of Introduction to Supervised Machine Learning
'Slide, Videos of Training and Test Splits
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 4: 2.2 Data Splits and Cross Validation
2.2.1 Training and Test Splits
Topic: Offline; Teaching type: CLO5; LO: T
ITU: Slide, Videos of Training and Test Splits
Materials: FLM and Coursera
Download: Read Slide and Watch Video
CLO3: Learners should be able to use a variety of techniques for detecting and dealing with outliers
Session 1: 2.2 Data Splits and Cross Validation
2.2.2 Cross Validation
Topic: Offline; Teaching type: CLO5; LO: T
ITU: Slide, Videos of Cross Validation
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 2: 2.3 Linear Regression
Topic: Offline; Teaching type: CLO4; LO: T
ITU: Slide, Videos of Linear Regression
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 3: 2.4 Polynomial Regression
2.5 Regression with Regularization Techniques
2.5.1 Regularization Techniques
Elastic Net, Recursive Feature Elimination (Homework)
Topic: Offline; Teaching type: CLO5; LO: T
ITU: Slide, Videos of Polynomial Regression
'Slide, Videos of Regularization Techniques
Materials: FLM and Coursera

Session 4: 2.5 Regression with Regularization Techniques
2.5.2 Polynomial Features and Regularization Demo
Topic: Offline; Teaching type: CLO5; LO: T
ITU: Slide, Videos of Regularization Techniques
Materials: FLM and Coursera
Download: Read Slide and Watch Video
CLO4: Learners should be able to describe and use supervised models
Session 1: 2.5 Regression with Regularization Techniques
2.5.2 Polynomial Features and Regularization Demo
Topic: Offline; Teaching type: CLO5; LO: T
ITU: Slide, Videos of Polynomial Features and Regularization Demo
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 2: 2.5 Regression with Regularization Techniques
2.5.3. Details of Regularization
Topic: Offline; Teaching type: CLO5; LO: T
ITU: Slide, Videos of Details of Regularization
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 3: Progress Test 2
Review Lab 1
Topic: Offline; Teaching type: ; LO: U
ITU: - Content 11 - 19.
- Progress test 2
- Student's lab
Download: - Review Content 11 - 19
- Do Progress test 2

Session 4: Review Lab 1
Topic: Offline; Teaching type: ; LO: U
ITU: Student's lab
Download: Presentation
CLO5: Learners should be able to use a variety of error metrics to compare and select a supervised model that best suits your data
Session 1: Review the mini Capstone
Topic: Offline; Teaching type: CLO10; LO: U
ITU: Student's capstone
Download: Presentation

Session 2: 3.1 Logistic Regression
3.1.1 Logistic Regression: Introduction to Classification and Error Metrics
Topic: Offline; Teaching type: CLO4, CLO5; LO: T
ITU: Slide, Videos of Logistic Regression: Introduction to Classification and Error Metrics
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 3: 3.1 Logistic Regression
3.1.2 Error Metrics
3.1.3 Logistic Regression Labs
Topic: Offline; Teaching type: CLO4, CLO5; LO: T
ITU: Slide, Videos of Error Metrics
'Slide, Videos of Logistic Regression Labs
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 4: 3.1 Logistic Regression
3.1.2 Logistic Regression Labs
Topic: Offline; Teaching type: CLO4, CLO5; LO: T
ITU: Slide, Videos of Logistic Regression Labs
Materials: FLM and Coursera
Download: Read Slide and Watch Video
CLO6: Learners should be able to use oversampling and undersampling as techniques to handle unbalanced classes in a data
Session 1: 3.2 K Nearest Neighbors
3.2.1 K Nearest Neighbors
Topic: Offline; Teaching type: CLO4, CLO5; LO: T
ITU: Slide, Videos of K Nearest Neighbors
Materials: FLM and Coursera
Download: Presentation

Session 2: 3.2 K Nearest Neighbors
3.2.1 K Nearest Neighbors
Topic: Offline; Teaching type: CLO4, CLO5; LO: T
ITU: Slide, Videos of K Nearest Neighbors
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 3: 3.2 K Nearest Neighbors
3.2.2 K Nearest Neighbors Labs
Topic: Offline; Teaching type: CLO4, CLO5; LO: T
ITU: Slide, Videos of K Nearest Neighbors Labs
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 4: 3.2 K Nearest Neighbors
3.2.2 K Nearest Neighbors Labs
Topic: Offline; Teaching type: CLO4, CLO5; LO: T
ITU: Slide, Videos of K Nearest Neighbors Labs
Materials: FLM and Coursera
Download: Read Slide and Watch Video
CLO7: Learners should be able to explain the kinds of problems suitable for Unsupervised Learning approaches
Session 1: Review lab 2
Topic: Offline; Teaching type: ; LO: U
ITU: Student's lab 2
Download: Presentation

Session 2: Review the mini Capstone
Topic: Offline; Teaching type: CLO10; LO: U
ITU: Student's capstone
Materials: FLM and Coursera
Download: Presentation

Session 3: 3.3 Support Vector Machines
3.3.1 Support Vector Machines
Topic: Offline; Teaching type: CLO4, CLO5; LO: T
ITU: Slide, Videos of Support Vector Machines
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 4: 3.3 Support Vector Machines
3.3.2 Support Vector Machines Kernels
Topic: Offline; Teaching type: CLO4, CLO5; LO: T
ITU: Slide, Videos of Support Vector Machines Kernels
Materials: FLM and Coursera
Download: Read Slide and Watch Video
CLO8: Explain the curse of dimensionality, and how it makes clustering difficult with many features
Session 1: 3.3 Support Vector Machines
3.3.3 Support Vector Machines Labs
Topic: Offline; Teaching type: CLO4, CLO5; LO: T
ITU: Slide, Videos of Support Vector Machines Labs
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 2: 3.3 Support Vector Machines
3.3.3 Support Vector Machines Labs
Topic: Offline; Teaching type: CLO4, CLO5; LO: T
ITU: Slide, Videos of Support Vector Machines Labs
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 3: 3.4 Decision Trees
3.4.1 Decision Trees
Topic: Offline; Teaching type: CLO4, CLO5; LO: T
ITU: Slide, Videos of Decision Trees
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 4: 3.4 Decision Trees
3.4.2 Decision Trees Labs
Topic: Offline; Teaching type: CLO4, CLO5; LO: T
ITU: Slide, Videos of Decision Trees Labs
Materials: FLM and Coursera
Download: Read Slide and Watch Video
CLO9: Learners should be able to describe and use common clustering and dimensionality-reduction algorithms
Session 1: 3.5 Ensemble Models
3.5.1 Ensemble Based Methods and Bagging
Topic: Offline; Teaching type: CLO4, CLO5; LO: T
ITU: Slide, Videos of Ensemble Based Methods and Bagging
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 2: 3.5 Ensemble Models
3.5.1 Ensemble Based Methods and Bagging
Topic: Offline; Teaching type: CLO4, CLO5; LO: T
ITU: Slide, Videos of Random Forest
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 3: 3.5 Ensemble Models
3.5.2 Bagging Labs
Topic: Offline; Teaching type: CLO4, CLO5; LO: T
ITU: Slide, Videos of Bagging Labs
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 4: 3.5 Ensemble Models
3.5.2 Bagging Labs
Topic: Offline; Teaching type: CLO4, CLO5; LO: T
ITU: Slide, Videos of Bagging Labs
Materials: FLM and Coursera
Download: Read Slide and Watch Video
Session 1: 3.5 Ensemble Models
3.5.3 Boosting and Stacking (Homework for Stacking)
Topic: Offline; Teaching type: CLO4, CLO5; LO: T
ITU: Slide, Videos of Boosting and Stacking
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 2: 3.5 Ensemble Models
3.5.4 Boosting and Stacking Labs
Topic: Offline; Teaching type: CLO4, CLO5; LO: T
ITU: Slide, Videos of Boosting and Stacking Labs
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 3: 3.6 Modeling Unbalanced Classes
3.6.1 Model Interpretability (Homework)
3.6.2 Modeling Unbalanced Classes
Topic: Offline; Teaching type: CLO4, CLO5; LO: T
ITU: Slide, Videos of Model Interpretability
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 4: 3.6 Modeling Unbalanced Classes
3.6.2 Modeling Unbalanced Classes
3.6.3 Practice Lab: Modeling Imbalanced Classes
Topic: Offline; Teaching type: CLO6; LO: T
ITU: Slide, Videos of Modeling Unbalanced Classes
Download: Read Slide and Watch Video
Session 1: Progress Test 3
Review the mini capstone
Topic: Offline; Teaching type: CLO10; LO: U
ITU: - Content 24 - 30 and 34 - 47.
- Progress test 3
- Student's capstone
Download: - Review Content 24 - 30 and 34 - 47
- Do Progress test 3
-

Session 2: Review the mini capstone
Topic: Offline; Teaching type: CLO10; LO: U
ITU: Student's capstone
Download: Presentation

Session 3: 4.1 Introduction to Unsupervised Learning and K Means
4.1.1 Introduction to Unsupervised Learning
4.1.2 K Means Clustering
Topic: Offline; Teaching type: CLO7, CLO8; LO: T
ITU: Slide, Videos of Introduction to Unsupervised Learning, K Means Clustering
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 4: 4.1 Introduction to Unsupervised Learning and K Means
4.1.2 K Means Clustering
4.1.3 K-Means Lab
Topic: Offline; Teaching type: CLO8; LO: T
ITU: Slide, Videos of K Means Clustering
Materials: FLM and Coursera
Download: Read Slide and Watch Video
Session 1: 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
Topic: Offline; Teaching type: CLO8, CLO9; LO: T
ITU: Slide, Videos of Computational hurdles of clustering algorithms
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 2: 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
Topic: Offline; Teaching type: CLO8, CLO9; LO: T
ITU: Slide , Videos of Common clustering algorithms
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 3: 4.2 Selecting a clustering algorithm
4.2.3 Compare clustering algorithms
Topic: Offline; Teaching type: CLO8, CLO9; LO: T
ITU: Slide, Video of Compare clustering algorithms
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 4: 4.2 Selecting a clustering algorithm
4.2.3 Compare clustering algorithms
Clustering Demo
Topic: Offline; Teaching type: CLO8, CLO9; LO: T
ITU: Slide, Video of Compare clustering algorithms
Materials: FLM and Coursera
Download: Read Slide and Watch Video
Session 1: 4.3 Dimensionality Reduction
4.3.1 Dimensionality Reduction
Topic: Offline; Teaching type: CLO8, CLO9; LO: T
ITU: Slide, Videos of Dimensionality Reduction
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 2: 4.3 Dimensionality Reduction
4.3.2 Principal Component Analysis
Topic: Offline; Teaching type: CLO8, CLO9; LO: T
ITU: Slide, Videos of Principal Component Analysis and Matrix Factorization
Materials: FLM and Coursera
Download: Read Slide and Watch Video

Session 3: 4.3 Dimensionality Reduction
4.3.2 Matrix Factorization (homework)
4.3.3 Principal Component Analysis Lab
Topic: Offline; Teaching type: CLO8, CLO9; LO: T
ITU: Slide, Videos of Principal Component Analysis and Matrix Factorization
Download: Read Slide and Watch Video

Session 4: Progress test 4
Review mini Capstone
Topic: Offline; Teaching type: CLO10; LO: U
ITU: - Content 50 - 58.
- Progress test 4
- Student's projects and ppt.
Download: https://flm.fpt.edu.vn/gui/role/student/- Review Content 50 - 58.- Do Progress test 4-
Session 1: Review the mini Capstone
Topic: Offline; Teaching type: CLO10; LO: U
ITU: Student's capstone and ppt.
Download: Presentation

Session 2: Review the mini Capstone
Topic: Offline; Teaching type: CLO10; LO: U
ITU: Student's capstone and ppt.
Download: Presentation

Session 3: Review
Topic: Offline; Teaching type: ; LO: U
ITU: All Slides
Materials: FLM and Coursera
Download: Review Slide

Session 4: Review
Topic: Offline; Teaching type: ; LO: U
ITU: All Slides
Materials: FLM and Coursera
Download: Review Slide