Faculty：TBD

Targeted Student：Undergraduate Students

Course Credit：TBD

Lecture Hours：48 Theoretical Hours

Course Type：专业必修课Compulsory Specialty Course

Related Preview Courses：

Calculus, Probability Theory and Linear Algebra

Introduction to AI and Machine Learning

2

Regression Problems and Least Square Estimation

3

Regression Problems and Gradient Descent

3

Logistic Regression and Maximum Likelihood Estimation

3

Bayesian Classifiers

4

Principal Component Analysis

3

Data Clustering

2

Neural Networks (Multilayer Perceptron)

5

Backpropagation Algorithm

2

Introduction to Deep Learning

3

Implementation of Neural Networks

6

Convolutional Neural Networks

6

Transfer Learning

3

Machine Learning Fever, Criticism and Wrap up.

3

（中文）人工智能导论

Teacher

Andrew Beng Jin Teoh

First day of classes

20 June 2022

Last day of classes

21 July 2022

Course Credit

2

Course Description

Course Introduction

Artificial Intelligence is one of the fastest-growing and most exciting fields lately, and machine learning represents its genuine bleeding edge. This course covers several traditional and deep learning models, e.g. linear and non-linear regression, logistic regression, Bayesian classifiers, principal component analysis, clustering, multilayer perceptron and convolutional neural networks. Methods to train and optimize the learning models and to perform effective inference will be highlighted. The course will cover the underlying theory and the range of applications to which machine learning has been applied.

Objective

1. Students will be able to learn several conventional and deep learning models.

2. Students will be able to learn how to apply machine learning methods for classification, feature extraction and regression.

Assignments (essay or other forms)

Quizzes and Final Project

Text Books and Reading Materials

1.Deep Learning/Goodfellow, Ian/MIT Press/2017

2.Dive into Deep Learning,https://d2l.ai/