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Week
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Topics
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Study Metarials
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1
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Introduction AI and ML
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K1-Chapter-1
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2
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Agent Architectures and Hierarchical Control
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K1-Chapter-2
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3
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Uninformed and Informed Search
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K1-Chapter-3
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4
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Knowledge Representation
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K1-Chapter-5
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5
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Linear regression: OLS, regularization, linear classifiers
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K2-Chapter-17.4
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6
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Logistic Regression, Multi-class logistic regression Ranking Support Vector Machines
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K2-Chapter-17.5
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7
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Decision Trees
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K2-Chapter-7
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8
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Feature selection latent factor models (PCA)
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K2-Chapter-15
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9
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Clustering (k-means, soft k-means)
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K2-Chapter-6
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10
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Ensemble methods such as Random Forest and Ada Boost
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K2-Chapter-5
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11
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Probabilistic methods (Bayesian view)
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K2-Chapter-1
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12
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Model evaluation and model selection
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K2-Chapter-9
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13
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Introduction to neural networks and convolutional neural networks
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K2-Chapter-10
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14
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Autoencoders
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K2-Chapter-11
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Prerequisites
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-
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Language of Instruction
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Turkish
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Responsible
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Asist. Prof. Dr. Mustafa KARHAN
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Instructors
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-
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Assistants
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-
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Resources
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K1- Poole, D. L., & Mackworth, A. K. (2010). Artificial Intelligence: Foundations of Computational Agents. (1th ed.). Cambridge University Press.
K2- Barber, D. (2012). Bayesian Reasoning and Machine Learning. (1th ed.). Cambridge University Press.
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Supplementary Book
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-
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Goals
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This module will introduce the foundational concepts in artificial intelligence and knowledge-based systems. This module also aims to provide students with an in-depth introduction to two main- areas of Machine Learning: supervised and unsupervised.
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Content
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Introduction to Artificial Intelligence and Machine Learning, Linear regression, Logistic Regression, Decision trees, ensemble methods such as random forest and AdaBoost, Model evaluation and model selection
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Program Learning Outcomes |
Level of Contribution |
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1
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Acquires information by carrying out scientific research in the field of Electronics and Computer Engineering, evaluates the findings and makes comments
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4
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2
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Complements the restricted or incomplete information and applies it, unifies the multidisciplinary information
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-
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3
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Designs and implements a system meeting the requirements in the field of Electronics and Computer Engineering
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-
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4
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Makes an interpretation of a problem in the field of Electronics and Computer Engineering, develops models for solutions and applies innovative methods in these solutions
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4
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5
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Has comprehensive knowledge on the contemporary applied method and techniques used in the field of Electronics and Computer Engineering and their limitations
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-
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6
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Undertakes and implements analytic, simulation or experimental types of research and has the ability to solve the complex problems encountered there
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3
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7
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Can participate and assume responsibility in multidisciplinary task forces
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-
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8
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Observes the scientific, professional and ethical rules during data collection, its introduction and interpretation
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-
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9
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Be aware of recent advances and developments in the field of Electronics and Computer Engineering learns, analyses and applies them wherever needed
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3
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10
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Publishes his/her research findings verbally and in written forms in the national and international arena
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-
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