Computer Science

This course explores modern numerical algorithms through three connected tasks: large scale linear algebra, optimization for data science, and deep learning.  The first six lectures discuss how to approximately solve massive scale linear algebra tasks using techniques not covered in linear algebra courses. The second six lectures discuss optimization algorithms with a focus on large data science tasks. Numerical optimization is one of the most useful skills as so many tasks from science to business can be cast as optimization problems. The six seminars focus on deep learning, the key algorithmic advance driving the recent advances in machine learning and artificial intelligence. The lectures on numerical linear algebra and optimization ground this course in well understood numerical algorithms which students can study in detail, while the deep learning seminars give students the opportunity to explore the excitement driving the AI revolution. 

This course offers a comprehensive exploration into the field of Artificial Intelligence (AI), specifically designed for students with diverse backgrounds. Spanning a period of three weeks, participants are introduced to fundamental AI concepts and techniques, ranging from basic machine learning principles to advanced neural networks and ethical considerations. Through a mix of interactive lectures, hands-on coding exercises, and practical case studies, students not only acquire a theoretical understanding of AI but also develop practical skills in data pre-processing, model implementation, and ethical decision-making. The course serves as a platform for students to delve into AI's potential and ethical dimensions, cultivating insights into its applications across industries and nurturing a curiosity for further AI study.

This course is intended for students whose work interacts with user interface issues in the design of social and software systems. The course stresses the importance of user-centered design and usability in the development of software applications and systems. Students will receive theoretical training on the analysis, design, and evaluation of user interfaces. They will also acquire hands-on design skills through a graphical user interface design project. The module takes into account contextual, organizational, and social factors in system design.

This course is a freshmen seminar, aiming to equip students with basic knowledge of the unique research and development methodologies, application scenarios, and hands-on practices of large language models (LLMs). The topics covered in the course include the using LLM for in-context learning, end-to-end application development using LLMs, fine- tuning, data management for AI, and development tools and services for large language models. The course consists of lectures and a significant amount of programming labs. Under the guidance of teaching assistants, students will complete several independent mini-experiments and team up to design a real-world LLM-based application. In this course, students will:

1) Learn how to use LLM for in-context learning with modern open-source frameworks; 2) Understand the fine-tuning methods of large language models, the usage of distributed training systems, and metrics to evaluate the quality of LLMs;
3) Learn the end-to-end practical development methods of LLM applications by designing and developing a non-trivial LLM application project;

4) Know the latest application scenarios of large language models and cutting-edge research problems in LLM;
5) Learn practical skills to work on a shared cloud computing environment;
6) Improve their team collaboration skills and project presentation skills.

This course offers students a grounding in the language of modern machine learning, with a focus on particular topics in linear algebra, differential calculus, probability, and statistics. Rather than focusing on theorems and their proofs, the course covers the key tools (and theorems) within the topic areas, and to illustrate these with exemplars drawn from machine learning. The course is delivered through a mixture of lectures and classes, and involves a mix of traditional lecture delivery, interactive notebooks, and problem sets.

The course provides an introduction to statistical analysis of text. Methods based on classic statistical approaches (including Bayesian models) and modern approaches such as deep learning (recurrent neural networks) are studied. Topics covered include preprocessing of textual data; text representation; text classification; text clustering; topic modeling; sentiment analysis; and text summarization.

This course is an introduction to algorithms. Lectures are about the fundamental skills of algorithm design and analysis. The course will teach the students how to analysis the asymptotic performance of algorithms with the growth of functions, as well as the probabilistic analysis and amortized analysis. Basic algorithm design skills such as divide-and-conquer, dynamic program functions and greedy algorithm are also included. Some specific topics, such as sorting algorithms, string matching algorithms, NP completeness theory and approximation algorithms will also be discussed.

This course is an introduction to problem-solving, algorithm development, and programming in the Python language. It includes fundamental programming constructs and abstractions, sorting and searching techniques, and machine representations of data. The practical component covers input/output, conditionals, loops, strings, functions, arrays, lists, dictionaries, recursion, text files, and exceptions in Python. Students are taught testing and debugging, as well as sorting and searching algorithms, algorithm complexity, and equivalence classes. Number systems, binary arithmetic, Boolean algebra, and logic gates are also introduced. The course is offered in a blended learning format. 

The major objective of this course is to teach you how to solve problems using algorithmic thinking with the concept of the "object-oriented" programming. We express our algorithms in English, then translate them into the programming language. We cover Python, C++ in this class. During the course, you learn how to use loops, conditionals, functions, arrays, and most importantly "classes." These are the building blocks of programs, which we use to create increasingly complex programs. This course is to understand the fundamentals of object-oriented programming; to understand how to use basic data structures and classes to create complex programs; and to develop problems solving skills by learning algorithmic thinking.

Prerequisite: CSI2100- Computer Programming