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Information
| Code | YZ004 |
| Name | Basic Concepts and Techniques of Artificial Intelligence |
| Term | 2024-2025 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | |
| Course Instructor |
1 |
Course Goal / Objective
This course aims to introduce students to the basic concepts, techniques and applications of artificial intelligence (AI).
Course Content
Students will learn the history of artificial intelligence, search algorithms, logical inference methods, machine learning basics, natural language processing (NLP) techniques and ethical aspects of artificial intelligence.
Course Precondition
Basic Logic Knowledge, Programming Knowledge
Resources
Stuart Russell ve Peter Norvig, Artificial Intelligence: A Modern Approach, Pearson, 4th Edition, 2021, 978-1292401133
Notes
Lecture notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Defines the differences between artificial intelligence, machine learning and deep learning. |
| LO02 | Students explain the history and basic concepts of artificial intelligence |
| LO03 | Explain and apply information-free search techniques (breadth-first search, depth-first search) |
| LO04 | Explain the basic concepts and solution techniques of constraint satisfaction problems (CSP) |
| LO05 | Construct and analyze knowledge-based systems using logical inference methods. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Beceriler - Bilişsel, Uygulamalı | To be able to access information broadly and deeply by conducting scientific research in the field, to be able to evaluate, interpret and apply the information. | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Has a comprehensive knowledge of current techniques and methods applied in engineering and their limitations. | 4 |
| PLO03 | Beceriler - Bilişsel, Uygulamalı | To be able to use uncertain, limited or incomplete data to complete and apply knowledge using scientific methods; to be able to use knowledge from different disciplines together. | 5 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Is aware of new and emerging practices of the profession, examines and learns them when needed. | |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Defines and formulates problems related to the field, develops methods to solve them and applies innovative methods in solutions. | 5 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Develops new and/or original ideas and methods; designs complex systems or processes and develops innovative/alternative solutions in their designs. | 4 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Designs and implements theoretical, experimental and modeling-based research; examines and solves complex problems encountered in this process. | 4 |
| PLO08 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | To be able to work effectively in disciplinary and multidisciplinary teams, to lead such teams and to develop solution approaches in complex situations; to be able to work independently and take responsibility. | |
| PLO09 | Bilgi - Kuramsal, Olgusal | To be able to communicate orally and in writing in a foreign language at least at the B2 level of the European Language Portfolio. | |
| PLO10 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | To be able to communicate the process and results of his/her studies systematically and clearly in written or oral form in national and international environments in or outside the field. | |
| PLO11 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Knows the social, environmental, health, safety, legal, project management and business life practices of engineering applications and is aware of the constraints these impose on engineering applications. | |
| PLO12 | Bilgi - Kuramsal, Olgusal | Observes social, scientific and ethical values in the stages of data collection, interpretation and announcement and in all professional activities. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to Artificial Intelligence: History of AI, definitions, Artificial Intelligence vs. Machine Learning vs. Deep Learning, important milestones. | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 2 | Intelligent Agents and Problem Solving: **: Agent types, environments, problem solving methods, search algorithms. | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 3 | Information-Free Search: Breadth-First Search (BFS), Depth-First Search (DFS), Unit Cost Search, Recursive Depth Search. | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 4 | Informed Search and Heuristics: A*, Greedy Best First Search, heuristics, validity and consistency | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 5 | Constraint Satisfaction Problems (CSP): CSP definition, backtracking, constraint propagation, local search | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 6 | Reciprocal Search: Game theory, minimax algorithm, alpha-beta pruning, stochastic games. | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 7 | Logical Agents: Knowledge-based agents, propositional logic, first-order logic, inference | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Preparation for the exam | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Planning and Motion in the Real World: Planning algorithms, partial sequence planning, planning graphs. | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 10 | Uncertainty in Artificial Intelligence: Probability theory, Bayesian networks, hidden Markov models. | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 11 | Fundamentals of Machine Learning: Supervised learning, unsupervised learning, reinforcement learning, decision trees, neural networks. | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 12 | Natural Language Processing (NLP): Text processing, language models, parsing, sentiment analysis. | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 13 | Ethics and Future of Artificial Intelligence: Ethical issues in AI, biases in AI systems, future trends. | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 14 | Evaluation of Current Practices and Tools | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 15 | Final Project Presentations: Students present their final projects. | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 16 | Term Exams | Preparation for the exam | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Preparation for the exam | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 15 | 15 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 20 | 20 |
| Total Workload (Hour) | 162 | ||
| Total Workload / 25 (h) | 6,48 | ||
| ECTS | 6 ECTS | ||