DEEP LEARNING

AI404

Course Code: AI404
Semester: -
Theory+Practice: 3 + 0
ECTS: 5

Course Type

Elective Course

Course Level

Bachelor's Degree

Year of Study

Semester

ECTS

Name of Lecturer(s)

Learning Outcomes

At the end of this course, the students;

Know deep learning methods such as convolutional neural networks, deep Boltzmann machines, and autoencoders.
Know how to design deep architectures for specific problems.
Know training and testing of deep neural networks.

Mode of Delivery

Prerequisites

Recommended Optional Programme Components

Course Description

Machine learning basics and applications. Training deep networks. Convolutional neural networks. Recurrent neural networks. Deep generative models.

Course Contents

WEEK	TOPICS
1^st Week	Machine learning basics and applications
2^nd Week	Overview of artificial neural networks
3^rd Week	Multilayer perceptron
4^th Week	Training deep networks
5^th Week	Convolutional neural networks
6^th Week	Convolutional neural networks
7^th Week	Recurrent neural networks
8^th Week	Midterm
9^th Week	Deep generative models
10^th Week	Deep generative models
11^th Week	Deep reinforcement learning
12^th Week	Deep reinforcement learning
13^th Week	Recent research topics and applications on audio-visual and language understanding.
14^th Week	Recent research topics and applications on audio-visual and language understanding.

Recommended or Required Reading

* Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, MIT Press, 2016
* K. P. Murphy. Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
* Tom Mitchell, "Machine Learning", McGraw-Hill, (1997).

Planned Learning Activities and Teaching Methods

Lecture
Questions/Answers
Problem Solving
Presentation

Assessment Methods and Criteria

	Quantity	Percentage (%)
Mid-term	1	30
Assignment	1	30
Total(%)		60
Contribution of In-term Studies to Overall Grade(%)		60
Contribution of Final Examination to Overall Grade(%)		40
Total(%)		100

Language of Instruction

Work Placement(s)

Programme Qualifications / Learning Outcomes

	LO1	LO2	LO3
K1
K2
K3
K4
K5
K6
K7
K8
K9
K10
K11
K12