COURSE UNIT TITLE

: DEEP LEARNING METHODS AND APPLICATIONS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
DSM 5014 DEEP LEARNING METHODS AND APPLICATIONS ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

Offered to

Data Science
Data Science (Non-Thesis-Evening)

Course Objective

To provide the students with theoretical and mostly applied study of; deep learning, artificial neural networks and relevant advanced topics in machine learning and data science. To establish in-depth knowledge of deep hierarchical models and learning mechanisms in computers, deep vs. shallow architectures, convolutional networks, LSTM and their applications to pattern recognition, speech recognition and natural language processing.

Learning Outcomes of the Course Unit

1   Implement and develop solutions for problems in companies or institutions by the aid of deep learning and convolutional neural networks.
2   Gain knowledge to effectively develop and implement convolutional and LSTM neural network models for data science applications.
3   Gain knowledge to effectively develop and implement deep learning models by using Python-based platforms such as TensorFlow, Keras, PyTorch, and so on.
4   Be able to describe and use different methodologies, procedures and techniques in deep learning.
5   Develop or implement research projects in the area of deep learning and convolutional neural networks.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Artificial Neural Networks. Biological neuron, Artificial neuron models, Perceptron learning, Multi-layer perceptron learning, Backpropagation. Supervised, semi-supervised and unsupervised learning.
2 Basics of Artificial Neural Networks. Gradient descent, Stochastic gradient descent, other optimization methods, Issues of convergence, over fitting, generalization.
3 Introduction to computer vision and pattern recognition. Hierarchies in human vision. The approaches using engineered-features, sparse encoding, multiple hierarchies, levels of the hierarchy and the responsibilities.
4 Introduction to Deep Learning. Auto-encoders. Learning representations with auto-encoders, influence of sparsity and issues of sparse data.
5 Convolutional Neural Networks. Basic theoretical concepts. Different layers of processing, convolution, pooling, drop-out, loss, training.
6 Applications of Convolutional Neural Networks.
7 Application development and coding for deep learning: Python language Part 1
8 Application development and coding for deep learning: Python language Part 2
9 Application development and coding for deep learning: Architectures, frameworks, and tools based on Python Part 1
10 Application development and coding for deep learning: Architectures, frameworks, and tools based on Python Part 2
11 Deep Recurrent Networks, LSTM, GRU and sequence learning. Simple Recurrent NN: Elman and Jordan networks. Standard Recurrent NN, Long/Short Term Memory.
12 Applications of Deep Recurrent Networks.
13 Deep vs. shallow learning. Advanced problems and current issues in deep learning and convolutional neural networks.
14 Project presentations. General discussion and review of the topics covered throughout the term.

Recomended or Required Reading

Y. Bengio, I. Goodfellow and A. Courville, Deep Learning, MIT Press, 2016.

L. Deng and D. Yu, Deep Learning: Methods and Applications , Foundations and Trends in Signal Processing, Now Publishers, 2014.

Supplementary Book(s): C. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.

K. P. Murphy, Machine learning: a probabilistic perspective, MIT press, 2012.

Planned Learning Activities and Teaching Methods

The course is taught in a lecture, class presentation, applied examples and exercises by using tools, and discussion format. Besides the taught lecture, group presentations are to be prepared by the groups assigned and presented in a discussion session. In some weeks of the course, results of the homework given previously are discussed.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 PRS PRESENTATION
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.40 +PRS * 0.20 +FIN * 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 +PRS * 0.20 +RST * 0.40


Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

mete.eminagaoglu@deu.edu.tr

Office Hours

Will be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparing assignments 2 30 60
Preparing presentations 2 12 24
Preparations before/after weekly lectures 13 4 52
Preparation for final exam 1 20 20
Final 1 2 2
TOTAL WORKLOAD (hours) 200

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7
LO.13534445
LO.25345445
LO.32454535
LO.44334343
LO.55434444

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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