[major=SE | course_id=AIL304m | syllabus_id=12035 | semester=7 | combo=SE_COM7.1 | title=Machine Learning]
Course ID: AIL304m
Title EN: Machine Learning
Title VI: Học máy
Credits: 3.0
Prerequisites: MAS291, MAE101
Description: In this course, student will build and train neural network, and you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow. Student make extensive use of tools from Linear Algebra, Calculus, Optimization.
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 https://flm.fpt.edu.vn/ for up-to-date course information
Tools: - Jupyter notebook
Course type: coursera_hybrid
Decision: 1286/QĐ-ĐHFPT dated 11/22/2024
Approved date: 2024-11-22
CLO1: Deeply understand the math that makes machine learning algorithms work.
[1] category=Lab; type=on-going; weight=20.0; duration=1 day; question_type=Lab1: CLO1
Lab 2: CLO2; grading_guide=Lab1: cover content of Module 1
Lab 2: cover content of Module 2; completion_criteria=; clos=>0
[2] category=Progress test; type=on-going; weight=20.0; duration=20'-30'; question_type=PT1: CLO1;
PT2: CLO2, CLO3, CLO4; grading_guide=PT1: cover content of Module 1;
PT2: cover content of Module 2,3,4; completion_criteria=; clos=>0
[3] category=Project; type=on-going; weight=30.0; duration=2 weeks; question_type=Project 1, 2: CLO2
Project 3: CLO4; grading_guide=Project 1, 2: cover content of Module 2
Project 3: cover content of Module 4; completion_criteria=; clos=>0
[4] category=Final exam; type=Final exam; weight=30.0; duration=60'; question_type=CLO1, CLO2, CLO3, CLO4,; grading_guide=all studied modules; completion_criteria=; clos=4
[1] desc=https://www.coursera.org/specializations/deep-learning; author=Coursera; publisher=Coursera; edition=; isbn=; main=True; online=True; note=
[2] desc=https://www.coursera.org/learn/neural-networks-deep-learning; author=Coursera; publisher=Coursera; edition=; isbn=; main=True; online=True; note=
[3] desc=https://www.coursera.org/learn/deep-neural-network?specialization=deep-learning; author=Coursera; publisher=Coursera; edition=; isbn=; main=True; online=True; note=
[4] desc=https://www.coursera.org/learn/machine-learning-projects?specialization=deep-learning; author=Coursera; publisher=Coursera; edition=; isbn=; main=True; online=True; note=
[5] desc=https://www.coursera.org/learn/machine-learning-linear-algebra?specialization=mathematics-for-machine-learning-and-data-science; author=Coursera; publisher=Coursera; edition=; isbn=; main=True; online=True; note=
[6] desc=https://www.coursera.org/learn/tensorflow-for-ai-get-to-know-tensorflow/ungradedLti/fsCCK/tensorflow-for-ai-get-to-know-tensorflow; author=Coursera; publisher=Coursera; edition=; isbn=; main=True; online=True; note=Project 1
[7] desc=https://www.coursera.org/projects/googlecloud-classify-images-of-cats-and-dogs-using-transfer-learning-6qgew; author=Coursera; publisher=Coursera; edition=; isbn=; main=True; online=True; note=Project 2
[8] desc=https://www.coursera.org/projects/introduction-to-fastapi-framework; author=Coursera; publisher=Coursera; edition=; isbn=; main=True; online=True; note=Project 3
Session 1: Math Introduction
1.1: Systems of Linear equations: two variables.
Topic: Offline; Teaching type: CLO1; LO: IT
ITU: Slide, Videos of Systems of Linear equations
Materials: AIL304m
Download: Read Slide and Watch Video

Session 2: Math Introduction
1.2: Systems of Linear Equations: three variables
Topic: Offline; Teaching type: CLO1; LO: IT
ITU: Slide, Videos of Systems of Linear equations
Download: Read Slide and Watch Video

Session 3: Solving systems of Linear Equations
1.3: Solving systems of Linear Equations: Elimination
Topic: Offline; Teaching type: CLO1; LO: IT
ITU: Slide, Videos of Solving systems of Linear Equations
Download: Read Slide and Watch Video

Session 4: 1.4: Solving systems of Linear Equations: Row Echelon Form and Rank
Topic: Offline; Teaching type: CLO1; LO: IT
ITU: Slide, Videos of Solving systems of Linear Equations
Download: Read Slide and Watch Video
CLO2: Able to build, train and apply fully connected deep neural networks, implement efficient neural networks, identify key parameters in a neural network's architecture, and apply deep learning to students' own applications.
Session 1: Vectors and Linear Transformations
1.5 Vectors
Topic: Offline; Teaching type: CLO1; LO: IT
ITU: Slide, Videos of Vectors and Linear Transformations
Download: Read Slide and Watch Video

Session 2: 1.6: Linear transformations
Topic: Offline; Teaching type: CLO1; LO: IT
ITU: Slide, Videos of Vectors and Linear Transformations
Download: Read Slide and Watch Video

Session 3: Programming Assignment: Single Perceptron Neural Networks for Linear Regression
Reset your deadline to
Topic: Offline; Teaching type: CLO1; LO: U
ITU: Exercise 1 file
Download: Do Exercise 1

Session 4: Determinants and Eigenvectors
1.7: Determinants In-depth
1.8: Eigenvalues and Eigenvectors
Topic: Offline; Teaching type: CLO1; LO: IT
ITU: Slide, Videos of Determinants and Eigenvectors
Download: Read Slide and Watch Video
CLO3: Able to train and develop test sets, analyze bias/variance for building deep learning applications, apply optimization algorithms, and implement a neural network using TensorFlow.
Session 1: Programming Assignment: Eigenvalues and Eigenvectors
Topic: Offline; Teaching type: CLO1; LO: U
ITU: Exercise 2 file
Download: Do Exercise 2

Session 2: Functions of one variable: Derivative and optimization
1.9: Derivatives
Topic: Offline; Teaching type: CLO1; LO: IT
ITU: Slide, Videos of Functions of one variable: Derivative and optimization
Download: Read Slide and Watch Video

Session 3: Functions of one variable: Derivative and optimization
1.10: Optimization with derivatives
Topic: Offline; Teaching type: CLO1; LO: IT
ITU: Slide, Videos of Functions of one variable: Derivative and optimization
Download: Read Slide and Watch Video

Session 4: Programming Assignment: Optimizing Functions of One Variable: Cost Minimization
Topic: Offline; Teaching type: CLO1; LO: U
ITU: Exercise 3 file
Download: Do Exercise 3
CLO4: Understand complex ML settings, diagnose errors in a machine learning system, prioritize strategies for reducing errors, and apply end-to-end learning, transfer learning, and multi-task learning.
Session 1: Functions of two or more variables: Gradients and gradient descent
1.11: Gradients and optimization
1.12: Gradient Descent
Topic: Offline; Teaching type: CLO1; LO: IT
ITU: Slide, Videos of Functions of two or more variables: Gradients and gradient descent
Download: Read Slide and Watch Video

Session 2: Functions of two or more variables: Gradients and gradient descent
1.13: Optimization in Neural Networks
Topic: Offline; Teaching type: CLO1; LO: IT
ITU: Slide, Videos of Functions of two or more variables: Gradients and gradient descent
Download: Read Slide and Watch Video

Session 3: Functions of two or more variables: Gradients and gradient descent
1.14:Beyond Gradient Descent: Newton’s Method
Topic: Offline; Teaching type: CLO1; LO: IT
ITU: Slide, Videos of Functions of two or more variables: Gradients and gradient descent
Download: Read Slide and Watch Video

Session 4: Lab01: Regression with Perceptron
Topic: Offline; Teaching type: CLO1; LO: U
ITU: Lab 1 file
Download: Do Lab 1
CLO5: Practical project with Tensorflow
Session 1: Review Lab01
Topic: Offline; Teaching type: CLO1; LO: U
ITU: Lab 1 file

Session 2: Progess test 1
Topic: Offline; Teaching type: CLO1; LO: U
ITU: - Content Module 1.
Download: Do progress test 1

Session 3: 2. Introduction to Artificial Intelligence and Machine Learning
Topic: Offline; Teaching type: CLO2; LO: IT
ITU: Slide , Videos of Introduction to Artificial Intelligence and Machine Learning
Download: Read Slide and Watch Video

Session 4: 2.1 Neural Networks Basics
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Neural Networks Basics
Download: Read Slide and Watch Video
Session 1: 2.2 Logistic Regression as a Neural Network
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Neural Networks Basics
Download: Read Slide and Watch Video

Session 2: 2.3 Python and Vectorization
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Neural Networks Basics
Download: Read Slide and Watch Video

Session 3: Programming Assignments: Logistic Regression with a Neural network mindset
Topic: Offline; Teaching type: CLO2; LO: U
ITU: Exercise 4 file
Download: Do Exercise 4 file

Session 4: 2.4 Shallow neural networks
Topic: Offline; Teaching type: CLO1; LO: T
ITU: Slide, Videos of Shallow neural networks
Download: Read Slide and Watch Video
Session 1: 2.4 Shallow neural networks
Topic: Offline; Teaching type: CLO1; LO: T
ITU: 0
Download: Read Slide and Watch Video

Session 2: Programming Assignments:Planar Data Classification with One Hidden Layer
Topic: Offline; Teaching type: CLO2; LO: U
ITU: Exercise 5 file
Download: Do Exercise 5

Session 3: Guidedline project 01: TensorFlow for AI: Get to Know Tensorflow
Topic: Offline; Teaching type: CLO4,CLO5; LO: U
ITU: Link in the Materials
Download: View in the link

Session 4: Guidedline project 01: TensorFlow for AI: Get to Know Tensorflow
Topic: Offline; Teaching type: CLO4,CLO5; LO: U
ITU: Link in the Materials
Download: View in the link
Session 1: Guidedline project 01: TensorFlow for AI: Get to Know Tensorflow
Topic: Offline; Teaching type: CLO4,CLO5; LO: U
ITU: Link in the Materials
Download: View in the link

Session 2: 2.5 Deep Neural Networks
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Deep Neural Networks
Download: Read Slide and Watch Video

Session 3: 2.5 Deep Neural Networks
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Deep Neural Networks
Download: Read Slide and Watch Video

Session 4: 2.5 Deep Neural Networks
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Deep Neural Networks
Download: Read Slide and Watch Video
Session 1: Review Project 1
Topic: Offline; Teaching type: CLO2; LO: U
ITU: Link in the Materials
Download: View in the link

Session 2: 2.5 Deep Neural Networks
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Deep Neural Networks
Download: Read Slide and Watch Video

Session 3: 2.5 Deep Neural Networks
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Deep Neural Networks
Download: Read Slide and Watch Video

Session 4: 2.5 Deep Neural Networks
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Deep Neural Networks
Download: Read Slide and Watch Video
Session 1: Guidedline project 2: Neural Network from Scratch in TensorFlow
Topic: Offline; Teaching type: CLO5; LO: U
ITU: Link in the Materials
Download: View in the link

Session 2: Guidedline project 2: Neural Network from Scratch in TensorFlow
Topic: Offline; Teaching type: CLO5; LO: U
ITU: Link in the Materials
Download: View in the link

Session 3: Guidedline project 2: Neural Network from Scratch in TensorFlow
Topic: Offline; Teaching type: CLO5; LO: U
ITU: Link in the Materials
Download: View in the link

Session 4: Lab 2 (Neural Networks)
Topic: Offline; Teaching type: CLO2; LO: U
ITU: Lab 2 file
Download: Do Lab 2
Session 1: Lab 2 (Neural Networks)
Topic: Offline; Teaching type: CLO2; LO: U
ITU: Lab 2 file
Download: Do Lab 2

Session 2: Lab 2 (Neural Networks)
Topic: Offline; Teaching type: CLO2; LO: U
ITU: Lab 2 file
Download: Do Lab 2

Session 3: 3.1 Practical aspects of Deep Learning
Topic: Offline; Teaching type: CLO,3; LO: T
ITU: Slide, Videos of Practical aspects of Deep Learning
Download: Read Slide and Watch Video

Session 4: 3.1 Practical aspects of Deep Learning
Topic: Offline; Teaching type: CLO3; LO: T
ITU: Slide, Videos of Practical aspects of Deep Learning
Download: Read Slide and Watch Video
Session 1: 3.2 Optimization algorithms
Topic: Offline; Teaching type: CLO3; LO: T
ITU: Slide, Videos of Optimization algorithms
Download: Read Slide and Watch Video

Session 2: 3.2 Optimization algorithms
Topic: Offline; Teaching type: CLO3; LO: T
ITU: Slide, Videos of Optimization algorithms
Download: Read Slide and Watch Video

Session 3: 3.2 Optimization algorithms
Topic: Offline; Teaching type: CLO3; LO: T
ITU: Slide, Videos of Optimization algorithms
Download: Read Slide and Watch Video

Session 4: Review Lab 2
Topic: Offline; Teaching type: CLO2; LO: U
ITU: Lab 2 file
Session 1: Review project 2
Topic: Offline; Teaching type: CLO5; LO: U
ITU: Link in the Materials
Download: View in the link

Session 2: 3.3 Hyperparameter tuning, Batch Normalization and Programming Frameworks
Topic: Offline; Teaching type: CLO3; LO: T
ITU: Slide, Videos of Hyperparameter tuning, Batch Normalization and Programming Frameworks
Download: Read Slide and Watch Video

Session 3: 3.3 Hyperparameter tuning, Batch Normalization and Programming Frameworks
Topic: Offline; Teaching type: CLO3; LO: T
ITU: Slide, Videos of Hyperparameter tuning, Batch Normalization and Programming Frameworks
Download: Read Slide and Watch Video

Session 4: 3.3 Hyperparameter tuning, Batch Normalization and Programming Frameworks
Topic: Offline; Teaching type: CLO3; LO: T
ITU: Slide, Videos of Hyperparameter tuning, Batch Normalization and Programming Frameworks
Download: Read Slide and Watch Video
Session 1: 3.3 Hyperparameter tuning, Batch Normalization and Programming Frameworks
Topic: Offline; Teaching type: CLO3; LO: T
ITU: Slide, Videos of Hyperparameter tuning, Batch Normalization and Programming Frameworks
Download: Read Slide and Watch Video

Session 2: Progess test 2
Topic: Offline; Teaching type: CLO2, CLO3; LO: U
ITU: - Content Module 2 and 3.
Download: Do Progress test 2

Session 3: 4.1 ML Strategy 1
Topic: Offline; Teaching type: CLO4; LO: T
ITU: Slide, Videos of ML Strategy 1
Download: Read Slide and Watch Video

Session 4: 4.1 ML Strategy 1
Topic: Offline; Teaching type: CLO4; LO: T
ITU: Slide, Videos of ML Strategy 1
Download: Read Slide and Watch Video
Session 1: 4.1 ML Strategy 1
4.2 ML Strategy 2
Topic: Offline; Teaching type: CLO4; LO: T
ITU: Slide, Videos of ML Strategy 1 and ML Strategy 2
Download: Read Slide and Watch Video

Session 2: Guidedline project 3: Deploy Models with TensorFlow Serving and Flask
Topic: Offline; Teaching type: CLO5; LO: U
ITU: Link in the Materials
Download: View in the link

Session 3: Guidedline project 3: Deploy Models with TensorFlow Serving and Flask
Topic: Offline; Teaching type: CLO5; LO: U
ITU: Link in the Materials
Download: View in the link

Session 4: Review project 3
Topic: Offline; Teaching type: CLO5; LO: U
ITU: Link in the Materials
Download: View in the link