HKU Summer Institute Data Science for Beginners: Theory, Algorithms and Applications"
Programme Code: IDSS2301
Welcome Notes from Instructors
Areas of interest: Machine Learning . Optimization . Statistics . Neural Network . Network Science
Data science has grown to become a large, interdisciplinary subject and an integral part of the society, with applications found across a wide range of domains. This summer course offers a friendly introduction to a number of important topics in data science, focusing on the mathematical and algorithmic foundations. Applications of data science in various scientific, engineering and business domains will also be presented.
- Understanding of the mathematical foundations of data science
- Ability to construct mathematical models for real-world data analytic problems
- Ability to process and analyze real-world, large-scale data sets
- Ability to implement data science related algorithms
- Appreciation of the broad and powerful applicability of data science
- Optimization: basic theory and modeling
- Optimization: algorithms
- Bayesian statistics
- Network theory
- Basics of machine learning
- Basics of neural network
Teaching Methods: Lectures, Programming Tutorials
|Week 1||July 10 (Monday)||July 11 (Tuesday)||July 12 (Wednesday)||July 13 (Thursday)||July 14 (Friday)|
|Morning Session (09:30-12:30)||Opening Ceremony||Performance Evaluation: Variance-Bias Trade-off||Nearest Neighbours||Linear Programming||Nonlinear Programming II: Algorithms|
|Afternoon Session (14:00-17:00)||Guest Lecture||Social Activities (Optional)||Bayes Classifiers||Nonlinear Programming I: Basics||Nonlinear Programming III: Constrained optimization|
|Week 2||July 17 (Monday)||July 18 (Tuesday)||July 19 (Wednesday)||July 20 (Thursday)||End of Programme|
|Morning Session (09:30-12:30)||Stochastic Methods in machine learning||Network models||Basics of Bayesian inference||Group Presentations|
|Afternoon Session (14:00-17:00)||Fundamentals of network theory||Social Activities (Optional)||Bayesian inference of the Stochastic Block Model||Closing Ceremony|
- Prof. Yi MA (guest lecturer)
- Dr. Sebastian MOREL BALBI
- Dr. Man Chung YUE
- Dr. Yue XIE
The programme content is subject to change without prior notice.
From Dr Man Chung YUE
Welcome to this summer course on data science. This course is a friendly introduction to data science. It will present various elementary mathematical topics that are important to data science and machine learning.
I’m looking forward to meeting all of you. I hope you’ll enjoy this course and have a wonderful trip in Hong Kong.
From Dr Yue XIE
Welcome to the exciting world of data science! We are thrilled to have you join our course on data science for beginners. Over the next two weeks, we will embark on a journey together, exploring the fundamental concepts and tools that form the backbone of this rapidly growing field.
Data science is a captivating discipline that empowers us to extract valuable insights and make informed decisions from vast amounts of data. Whether you are a novice with no prior experience or have a background in a different field, this course is designed to provide you with a solid foundation in data science principles.
Optimization lies at the heart of data science, enabling us to find the best possible solutions to complex problems. Throughout my part of the course, we will delve into various optimization algorithms, from basic methods like one-dimensional line search to advanced stochastic gradient descent. You will gain a deep understanding of how these algorithms work, their strengths, and their limitations.
In addition to optimization algorithms, we will cover essential topics such as linear programming, nonlinear programming, convex optimization, and integer programming. We will also explore how optimization techniques are applied in machine learning, neural networks, and resource allocation, among other domains.
Our dedicated team is committed to your success and will be there to guide you every step of the way. We encourage you to actively participate in class discussions, ask questions, and collaborate with your fellow students. Remember, learning is a collaborative process, and we believe that the collective knowledge and experiences of our diverse community will enhance everyone’s learning journey.
Get ready to embark on an exhilarating journey and unleash your problem-solving prowess. We are thrilled to have you on board and look forward to an enriching and rewarding learning experience together.
From Dr Sebastian Morel-Balbi
Welcome to the University of Hong Kong’s Summer Institute on Data Science! We are happy to have you join us for this exciting and comprehensive program designed to explore various aspects of data science and its applications.
As a postdoctoral researcher in complex networks, I am delighted to lead the seminar series on inferential techniques applied to complex networks. We will dive into the intricate world of interconnected systems, exploring how inferential techniques can unlock valuable insights into their structure, dynamics, and behaviour. Our focus will be on understanding the models, principles and methods that enable us to draw meaningful inferences from complex networks.
The course will provide you with a unique perspective on inferential techniques tailored to the complex network domain. We will aim to understand how specific characteristics commonly exhibited by real-world networked systems can affect information diffusion and network resilience and will delve into topics such as network modelling and community detection. Through a combination of lectures and hands-on exercises, you will develop a solid understanding of how inferential techniques are used to extract useful information from complex networks.
This Summer Institute offers a rich opportunity for interdisciplinary learning and collaboration. By interacting with students and experts from various backgrounds, you will gain valuable insights and broaden your understanding of the field as a whole.
Once again, welcome to the HKUSI on Data Science!