Course Overview

Topics: optimization problems, regression, logistic regression, biological neural networks, model selection, universal approximation theorem, perceptron, MLP, backpropagation, deep neural networks, recurrent neural networks, LSTM, Hopfield network, Bolzmann machine

Learning Achievement

In this class, students will, firstly, gain a theoretical understanding of the principles underlying the methods applied to neural networks and, secondly, learn practical skills in implementing neural networks and applying them for data analysis.

Competence

In this class, students will, firstly, gain a theoretical understanding of the principles underlying the methods applied to neural networks and, secondly, learn practical skills in implementing neural networks and applying them for data analysis.

Course prerequisites

Calculus, linear algebra, statistics, programming

Grading Philosophy

Final Exam

Course schedule

Week1: Introduction
Week 2: followed by Week 3 to the Final Week

Course type

combination of lecture and exercise

Online Course Requirement

communication platform: moodle

Instructor

Prof. Sen Cheng

Other information

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