[major=AI | course_id=ASR301c | syllabus_id=12628 | semester=5 | combo=AI17_COM3 | title=AI for Scientific Research]
Course ID: ASR301c
Title EN: AI for Scientific Research
Title VI: TTNT cho Nghiên cứu khoa học
Credits: 3.0
Prerequisites: 
Description: This course is about using AI in scientific situations to discover trends and patterns within datasets. Course 1 teaches a little about the Python language related to data science. We'll share some existing libraries to help analyze your datasets. By the end of the course, you'll apply a classification model to predict the presence or absence of heart disease from a patient's health data. Course 2 covers the complete machine learning pipeline, from reading in, cleaning, and transforming data to running basic and advanced machine learning algorithms. In the final project, we'll apply our skills to compare different machine learning models in Python. In Course 3, we will build on our knowledge of basic models and explore more advanced AI techniques. We’ll describe the differences between the two techniques and explore how they differ. Then, we’ll complete a project predicting similarities between health patients using random forests. In Course 4, a capstone project course, we'll compare genome sequences of COVID-19 mutations to identify potential areas a drug therapy can look to target. By the end, you'll be well on your way to discovering ways to combat disease with genome sequencing.
Student tasks: Student must get the certification of AI for Scientific Research Specialization from Coursera to be accepted to the final examination
Tools: 
Course type: coursera_only
Decision: 359/QĐ-ĐHFPT dated 04/09/2025
Approved date: 2025-04-09
CLO1: Employ artificial intelligence techniques to test hypothesis in Python, and apply a machine learning model combining Numpy, Pandas, and Scikit-Learn
[1] category=Final exam; type=Final exam; weight=100.0; duration=60'; question_type=CLO1, CLO2, CLO3, CLO4; grading_guide=All studied courses. Each module of course contributes 4-5 questions.; completion_criteria=Final TE Score >=4 & (Final TE Score + bonus) >= 5
4. Complete the online courses and get all specialization certifications to be allowed to take Final Exam; clos=4
[1] desc=Specialization: AI for Scientific Research Specialization; author=LearnQuest; publisher=Coursera; edition=; isbn=; main=True; online=True; note=https://www.coursera.org/specializations/artificial-intelligence-scientific-research
[2] desc=MOOC 1: Introduction to Data Science and scikit-learn in Python; author=LearnQuest; publisher=Coursera; edition=; isbn=; main=True; online=True; note=https://www.coursera.org/learn/data-science-and-scikit-learn-in-python?specialization=artificial-intelligence-scientific-research
[3] desc=MOOC 2: Machine Learning Models in Science; author=LearnQuest; publisher=Coursera; edition=; isbn=; main=True; online=True; note=https://www.coursera.org/learn/machine-learning-models-in-science?specialization=artificial-intelligence-scientific-research
[4] desc=MOOC 3: Neural Networks and Random Forests; author=LearnQuest; publisher=Coursera; edition=; isbn=; main=True; online=True; note=https://www.coursera.org/learn/neural-networks-random-forests?specialization=artificial-intelligence-scientific-research
[5] desc=MOOC 4: Capstone Project: Advanced AI for Drug Discovery; author=LearnQuest; publisher=Coursera; edition=; isbn=; main=True; online=True; note=https://www.coursera.org/learn/ai-for-drug-discovery?specialization=artificial-intelligence-scientific-research
[6] desc=Help Articles; author=; publisher=Coursera; edition=; isbn=; main=; online=True; note=https://www.coursera.support/s/article/208280036-Coursera-Code-of-Conduct?language=en_US
Session 1: Specialization Introduction
Introduction to Data Science and scikit-learn in Python
- Introduction to Python Programming for Hypothesis Testing
- Creating a Hypothesis: Numpy, Pandas, and Scikit-Learn
Offline 1: Guide learning and review of the specialization
Topic: Offline; Teaching type: CLO1; LO: IT
ITU: Associated Coursera links
Download: Complete Week 1, 2 of the course
Introduction to Data Science and scikit-learn in Python

Session 2: Introduction to Data Science and scikit-learn in Python (cont.)
- Scikit-Learn Revisited: ML for Hypothesis Testing
- Using Classification to Predict the Presence of Heart Disease
Topic: Online; Teaching type: CLO1; LO: I
ITU: Associated Coursera links
Download: Complete Week 3, 4 of the course Introduction to Data Science and scikit-learn in Python

Session 3: Machine Learning Models in Science
- Before the AI: Preparing and Preprocessing Data
Topic: Online; Teaching type: CLO2; LO: IT
ITU: Associated Coursera links
Download: - Complete Week 1 of the course Machine Learning Models in Science

Session 4: Machine Learning Models in Science (cont.)
- Foundational AI Algorithms: K-Means and SVM
Topic: Online; Teaching type: CLO2; LO: I
ITU: Associated Coursera links
Download: Complete Week 2 of the course Machine Learning Models in Science
CLO2: Implement and evaluate machine learning models (neural networks, random forests, etc.) on scientific data in Python
Session 1: Machine Learning Models in Science (cont.)
- Advanced AI: Neural Networks and Decision Trees
Topic: Online; Teaching type: CLO2; LO: I
ITU: Associated Coursera links
Download: Complete Week 3 of the course Machine Learning Models in Science

Session 2: Machine Learning Models in Science (cont.)
- Course Project
Topic: Online; Teaching type: CLO2; LO: I
ITU: Associated Coursera links
Download: Complete Week 4 of the course Machine Learning Models in Science

Session 3: Neural Networks and Random Forests
- I2NN and Deep dive into NN
Topic: Online; Teaching type: CLO3; LO: IT
ITU: Associated Coursera links
Download: - Complete Week 1 of the course Neural Networks and Random Forests

Session 4: Neural Networks and Random Forests (cont.)
- Exploring Random Forests
Topic: Online; Teaching type: CLO3; LO: I
ITU: Associated Coursera links
Download: Complete Week 2 of the course Neural Networks and Random Forests
CLO3: Implement simple neural network models and describe the differences between Neural Networks and Random Forests
Session 1: Neural Networks and Random Forests (cont.)
- Final Project: Comparing Models to predict pedal width
Topic: Online; Teaching type: CLO3; LO: I
ITU: Associated Coursera links
Download: Complete Week 3 of the course Neural Networks and Random Forests

Session 2: Capstone Project: Advanced AI for Drug Discovery
- Comparing genome sequences and PCA on genome sequence
Topic: Online; Teaching type: CLO4; LO: IT
ITU: Associated Coursera links
Download: - Complete Week 1 of the course Capstone Project: Advanced AI for Drug Discovery

Session 3: Capstone Project: Advanced AI for Drug Discovery (cont.)
- Feature Analysis using K-means Clustering
Topic: Online; Teaching type: CLO4; LO: I
ITU: Associated Coursera links
Download: Complete Week 2 of the course Capstone Project: Advanced AI for Drug Discovery

Session 4: Capstone Project: Advanced AI for Drug Discovery (cont.)
- Predicting Bit Score to find sequences matches
Course 4 Review
Topic: Offline; Teaching type: CLO4; LO: IT
ITU: Associated Coursera links
Download: https://flm.fpt.edu.vn/gui/role/student/- Complete Week 3 of the course Capstone Project: Advanced AI for Drug Discovery- Deadline of Coursera Completion: End of Friday of the week.All Students should participate in review slot (if any question)
CLO4: Analyzing genome sequences to find similarities and identify target subsequences using predctive models