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Chapter 1. Transformers, Large Language Models, and ChatGPT.pdf
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Chapter2. Large Language Models and Recommendation Algorithms.pdf 2.1 The Vector Space Model, bag of words and Tokenization 2.2 Word Embedding, Word2Vec and skip-grams approaches 2.3 Recommendations based on Content Data 2.4 Decision Tree Classifier 2.5 Feature Selection 2.6 Naive Bayes Classifier 2.7 Python Exercise: Recommend Friends based on the CBF Algorithm Chapter2.ppt |
Chapter3. Collaborative Filtering Algorithms.pdf
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Chapter4. Context-aware AI Systems.pdf 4.1 Sequence-aware AI Systems (Sequencial Transformers) 4.2 Location-aware AI Systems 4.3 AI systems for LBSNs 4.4 Types of Recommendations in LBSNs 4.5 Hybrid and Ensemble Methods 4.6 Types of Hybrid Systems 4.7 Python Exercise: Recommend Friends based on a Hybrid Algorithm Chapter4.pptx |
Chapter 5. Matrix Decomposition Algorithms.pdf
5.1 Eigenvalue Decomposition 5.2 Singular Value Decomposition 5.3 From SVD to UV-decomposition 5.4 Tensor Decomposition 5.5 From SVD to UV-decomposition 5.6 Tensor Decomposition 5.7 Tucker Decomposition 5.8 Python Exercise: Apply UV-decomposition to a User-Item Rating Matrix Chapter 5.pptx |
Chapter 7. Deep Reinforcement Learning.pdf 7.1 Q-learning Algorithm 7.2 Step-by-Step Execusion of the Q-learning Algorithm 7.3 Deep Reinforcement Learning 7.4 Deep Q-Network with Experience Replay Algorithm 7.5 Advantage Actor Critic Algorithm 7.6 Python Exercise 1: Implement the Tabular Q-learning Algorithm 7.7 Python Exercise 2: Implement the DQN Algorithm Chapter7.pptx Python code in Google Colab |
Chapter 8. Deep Graph Neural Networks.pdf 8.1 Graphs Fundamentals 8.2 Local-based Similarity Measures 8.3 Global-based Similarity Measures 8.4 Knowledge Graphs 8.5 Graph Convolutional Networks 8.6 Python Exercise: Graph-based Recommendations for an Online Newspaper chapter 8.pptx
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Chapter 9. Εvaluation Metrics of AI systems.pdf 9.1 Introduction to AI Models' Evaluation 9.2 MAE and RMSE 9.3 Precision, Recall and F1 metric 9.4 ROC curve and AUC metric 9.5 Normalized Discounted Cumulative Gain 9.6 Beyond Accuracy Metrics 9.7 Python Exercise: Build an Evaluation Framework chapter9.pptx
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Chapter 10. New Trends in LLMs and Recommender Systems.pdf 10.1 From LLMs to LMMs (multi-modal), ChatGPT and Med Gemini 10.1.1. Audio transformer, Word transformer, Video transformer, Image transformer. 10.2 Ethics in AI Systems 10.3 Fairness, Accountability, Censorship 10.4 Privacy and AI Systems 10.5 Systems' Architecture for Privacy 10.6 Algorithmic Techniques for Privacy Protection 10.7 Legal Framework for Privacy Protection Chapter10.pptx |
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