This course is to introduce the principles of various machine learning (ML) techniques. It covers topics such as the formulation of learning problems and various applications of ML. The concepts are practiced using the tools from Scikit-Learn and Tensorflow, in exercises and a project.
See you in class!
(masirasyafila.tan@xmu.edu.my)
This course provides the basic knowledge of artificial intelligence and related mathematics. The topics include searching, heuristic searching, simulated annealing, gradient method, computational intelligence, machine learning and deep learning components.
This course is an introductory laboratory that explores the design, construction, and debugging of circuits. It covers fundamentals of basic electrical and electronic circuits, with instruments, components, and power supply.
This course delves into the intricate world of semiconductor packaging. It covers various aspects, including package types (such as flip-chip), materials (die attach, substrate, and encapsulation), design considerations (thermal management, signal integrity, and power delivery), and reliability (stress testing, failure modes, and mitigation strategies). Students explore substrate-level interconnections, wire bonding, and advanced packaging technologies like 2.5D and 3D integration.
The course aims to introduce algorithmic techniques that form the foundations of processing and analysing massive datasets of various forms. In particular, the course discusses how to pre-process massive datasets, efficiently store massive datasets, design fast algorithms for massive datasets, and analyse the performance of designed algorithms.
The course aims to introduce algorithmic techniques that form the foundations of processing and analysing massive datasets of various forms. In particular, the course discusses how to pre-process massive datasets, efficiently store massive datasets, design fast algorithms for massive datasets, and analyse the performance of designed algorithms.
This course covers the fundamentals of software development, software process models, software system design, large-scale software system development, and software development environments. This course will focus on using Unified Modeling Language (UML) to perform analysis and design for Software System.
This course covers advanced and latest research issues in Natural Language Processing, including Sentiment Analysis, Topic Modeling, Text Clustering, Text Classification, Information Extraction, Dialogue Systems, Question Answering, Summarization, Text Generation, Knowledge Graphs, and Machine Translation. The discussion of these advanced topics will include some of the latest pre-trained models, such as BERT, Hugging Face, GPT, and ChatGPT.