In this course, fundamental concepts and applications of information retrieval and natural language processing will be discussed. Students will gain knowledge on information retrieval (IR) techniques (tf-idf, BM25, etc.), data models (i.e. vector space model, probabilistic models, word2vec, GloVe) for IR, Semantic Web for data representation and retrieval. In addition, students will gain knowledge on theory (i.e. stemming, morphological, syntactic, semantic analysis, etc.) and applications (i.e. document clustering, question answering, sentiment analysis, etc.) of Natural Language Processing (NLP). Furthermore, students will investigate recent trends of deep learning for IR and NLP, such as neural networks, transformers, attention networks, graph convolutional neural networks, LSTM and Bidirectional Encoder Representations from Transformers (BERT). By the end of the course students will learn both theoretical and applications of IR and NLP. In addition, student will choose a topic, and will have hands-on experience by developing a project in a relevant field.