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COURSE SYLLABUS
MATHEMATICAL FOUNDATIONS OF COMPUTING
1 Course Title: MATHEMATICAL FOUNDATIONS OF COMPUTING
2 Course Code: BM6021
3 Type of Course: Optional
4 Level of Course: Third Cycle
5 Year of Study: 1
6 Semester: 1
7 ECTS Credits Allocated: 6
8 Theoretical (hour/week): 3
9 Practice (hour/week) : 0
10 Laboratory (hour/week) : 0
11 Prerequisites: None
12 Recommended optional programme components: None
13 Language: Turkish
14 Mode of Delivery: Face to face
15 Course Coordinator: Dr. Ögr. Üyesi CEYDA NUR ÖZTÜRK
16 Course Lecturers: Prof. Dr. Kemal FİDANBOYLU
17 Contactinformation of the Course Coordinator: ceydanur@uludag.edu.tr
18 Website:
19 Objective of the Course: To teach the mathematical approaches that form the basis and are useful in design, learning, or evaluation processes of artificial intelligence methods, and in this context to carry out thorough examinations of signal analysis, probability theorem, optimization techniques, decision processes, statistical tests, information theory, fuzzy logic, belief theory, decision processes, and deep networks.
20 Contribution of the Course to Professional Development To have students comprehend the operational logic behind commonly used artificial intelligence methods and build the necessary mathematical foundations to design similar intelligent methods.
21 Learning Outcomes:
1 Knowing the requisites and deficiencies of the artificial intelligence systems;
2 Being able to analyze real problems so as to solve them with artificial intelligence systems;
3 Being able to associate probability, information, and belief theories with representation, learning, or testing phases of the intelligent systems;
4 Being knowledgeable about optimization techniques that enable learning;
5 Being able to describe the differences between the fuzzy logic-based and probability-based applications;
6 Being able to learn empirical or statistical models with decision trees, artificial neural networks, or deep networks;
7 Being able to ground the evolution of learning strategies from artificial neural networks to deep networks;
22 Course Content:
Week Theoretical Practical
1 Limits of artificial intelligence, human-level artificial intelligence, expert systems
2 Signal terminology and signal characteristics
3 Probability theory, conditional probability, Bayes’ theorem, independence
4 Probability-based classifiers, data preparation, evaluation
5 Basics of learning and optimization techniques
6 Optimization in artificial neural networks, backpropagation, network parameters
7 Random processes and decision making
8 Statistical tests, information theory
9 Fuzziness and belief theory
10 Applications of fuzzy logic
11 Hopfield networks, Boltzmann machines
12 Deep restricted Boltzmann machines
13 Variational autoencoders
14 Reinforcement learning, Markov decision processes
23 Textbooks, References and/or Other Materials: 1. Jackson, P. C., 2019. Toward Human-Level Artificial Intelligence, Dover Publications Inc, ISBN-10: 0486833003 ISBN-13: 978-0486833002.
2. Bender, E. A., 1996. Mathematical Methods Artificial Intelligence, Wiley-IEEE Computer Society Pr, ISBN-10: 0818672005 ISBN-13: 978-0-818-67200-2.
3. Stone, J. V., 2019. Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning, Sebtel Press, ISBN-10: 0956372813 ISBN-13: 978-0956372819.
24 Assesment
TERM LEARNING ACTIVITIES NUMBER PERCENT
Midterm Exam 1 20
Quiz 0 0
Homeworks, Performances 3 30
Final Exam 1 50
Total 5 100
Contribution of Term (Year) Learning Activities to Success Grade 50
Contribution of Final Exam to Success Grade 50
Total 100
Measurement and Evaluation Techniques Used in the Course written exams, assignments, research report, presentation
Information
25 ECTS / WORK LOAD TABLE
Activites NUMBER TIME [Hour] Total WorkLoad [Hour]
Theoretical 14 3 42
Practicals/Labs 0 0 0
Self Study and Preparation 14 3 42
Homeworks, Performances 3 18 54
Projects 0 0 0
Field Studies 0 0 0
Midtermexams 1 16 16
Others 0 0 0
Final Exams 1 24 24
Total WorkLoad 178
Total workload/ 30 hr 5,93
ECTS Credit of the Course 6
26 CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS
PQ1 PQ2 PQ3 PQ4 PQ5 PQ6
LO1 1 0 1 0 0 0
LO2 1 1 2 0 0 0
LO3 0 1 2 0 0 0
LO4 1 2 2 0 0 0
LO5 1 1 1 0 0 0
LO6 1 2 1 0 0 0
LO7 2 2 2 0 0 0
LO: Learning Objectives PQ: Program Qualifications
Contribution Level: 1 Very Low 2 Low 3 Medium 4 High 5 Very High
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