AI for Scientific Research (ASR301c)
Vietnamese title: TTNT cho Nghiên cứu khoa học
Credits: 3

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.

Learning outcomes:
Employ artificial intelligence techniques to test hypothesis in Python, and apply a machine learning model combining Numpy, Pandas, and Scikit-Learn
Implement and evaluate machine learning models (neural networks, random forests, etc.) on scientific data in Python
Implement simple neural network models and describe the differences between Neural Networks and Random Forests
Analyzing genome sequences to find similarities and identify target subsequences using predctive models

Student tasks:
Student must get the certification of AI for Scientific Research Specialization from Coursera to be accepted to the final examination

Tools:

Materials:
description: Specialization: AI for Scientific Research Specialization
author: LearnQuest
publisher: Coursera
note: https://www.coursera.org/specializations/artificial-intelligence-scientific-research
description: MOOC 1: Introduction to Data Science and scikit-learn in Python
author: LearnQuest
publisher: Coursera
note: https://www.coursera.org/learn/data-science-and-scikit-learn-in-python?specialization=artificial-intelligence-scientific-research
description: MOOC 2: Machine Learning Models in Science
author: LearnQuest
publisher: Coursera
note: https://www.coursera.org/learn/machine-learning-models-in-science?specialization=artificial-intelligence-scientific-research
description: MOOC 3: Neural Networks and Random Forests
author: LearnQuest
publisher: Coursera
note: https://www.coursera.org/learn/neural-networks-random-forests?specialization=artificial-intelligence-scientific-research
description: MOOC 4: Capstone Project: Advanced AI for Drug Discovery
author: LearnQuest
publisher: Coursera
note: https://www.coursera.org/learn/ai-for-drug-discovery?specialization=artificial-intelligence-scientific-research
description: Help Articles
publisher: Coursera
note: https://www.coursera.support/s/article/208280036-Coursera-Code-of-Conduct?language=en_US

Assessments:
category: Final exam
type: Final exam
weight: 100.0
duration: 60'
question_type: CLO1, CLO2, CLO3, CLO4
no_question: Multiple choicesMarked by Computer
knowledge_and_skill: 50
grading_guide: All studied courses. Each module of course contributes 4-5 questions.
note: by exam board,
using computer

Schedule:
session: 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
learning_outcomes: IT
session: 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
learning_outcomes: I
session: Machine Learning Models in Science
- Before the AI: Preparing and Preprocessing Data
learning_outcomes: IT
session: Machine Learning Models in Science (cont.)
- Foundational AI Algorithms: K-Means and SVM
learning_outcomes: I
session: Machine Learning Models in Science (cont.)
- Advanced AI: Neural Networks and Decision Trees
learning_outcomes: I
session: Machine Learning Models in Science (cont.)
- Course Project
learning_outcomes: I
session: Neural Networks and Random Forests
- I2NN and Deep dive into NN
learning_outcomes: IT
session: Neural Networks and Random Forests (cont.)
- Exploring Random Forests
learning_outcomes: I
session: Neural Networks and Random Forests (cont.)
- Final Project: Comparing Models to predict pedal width
learning_outcomes: I
session: Capstone Project: Advanced AI for Drug Discovery
- Comparing genome sequences and PCA on genome sequence
learning_outcomes: IT
session: Capstone Project: Advanced AI for Drug Discovery (cont.)
- Feature Analysis using K-means Clustering
learning_outcomes: I
session: Capstone Project: Advanced AI for Drug Discovery (cont.)
- Predicting Bit Score to find sequences matches
Course 4 Review
learning_outcomes: IT