NLP and Text Sentiment Analysis

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NLP and Text Sentiment Analysis

This lecture overviews Natural Language Processing (NLP) and Text Sentiment Analysis that has many applications in Text Analytics, Opinion extraction, Opinion mining, Sentiment mining, Subjectivity analysis. It covers the following topics in detail: Baseline algorithms. Text pre-processing. Word embeddings (Word2Vec, Fast Text). Neural NLP and Sentiment Analysis for text classification (RNN, CNN). Contextual Embeddings (ELMo). BERT.

Natural Language Processing

This lecture overviews Natural Language Processing (NLP) that has many applications in text analytics, Linguistics, Machine translation and sentiment analysis. It covers the following topics in detail: Symbolic NLP, Statistical NLP, Neural NLP. NLP methods: Rules, Statistics, Neural networks. Word Representations: Fixed (sparse), One-hot encoding, Bag-of-words, TF-IDF Distributed (dense). Classic embeddings: Word2Vec, GloVe, FastText. Contextualized embeddings: CoVe, ELMo, OpenAI GPT, BERT. Common NLP Tasks: Automatic summarization, Book generation, Question answering, Machine translation.

Neural Speech Recognition

This lecture overviews Neural Speech Recognition is a special case of Automatic Speech Recognition (ASR), i.e., the transcription of speech to text that has many applications e.g., in call centers, dictation, meeting minutes creation, Smart assistants (Apple’s Siri, Amazon’s Alexa, Google Assistant, Microsoft’s Cortana) and in Behavior /emotion recognition. It covers the following topics in detail:  Neural Speech Recognition Datasets. Neural Speech Recognition Methods. Deep Neural Network (RNN, CNN, Transformer) methods.

Music Genre Recognition

This lecture overviews Music Genre Recognition that has many applications in the music industry and in the social/broadcasted media. It covers the following topics in detail: Audio Feature Extraction. Music Spectrograms. Sound Texture Selection. Machine Learning Algorithms. Gaussian Processes. Support Vector Machines. Music Recognition using Deep Neural Networks.

Genomic Signal Analysis

This lecture overviews Genomic Signal Analysis   that has many applications in Bioinformatics, Biology and Medicine. It covers the following topics in detail: DSP Algorithms for Genomic Sequences. Numerical representation of genomic sequences. DNA string analysis: Long range correlations in DNA, Identification of protein coding DNA regions, Signal Extraction for DNA microarray, Alignment methods, Phylogenetic analysis. Machine Learning in Genomic Signal Analysis. RNA string analysis. Protein string analysis. 3D Protein Folding.

ECG Signal Analysis

This lecture overviews ECG Signal Analysis as well as other cardiology imaging methods that has many applications in cardiological disorder diagnosis and treatment. It covers the following topics in detail: Background nnowledge of ECG Signals. Issues in ECG Classification. Materials and Machine Learning Methods: Datasets, Data Preprocessing, Feature Selection, Dimensionality Reduction, Machine Learning Classifiers, Validation and Evaluation Analysis of Classifiers. Deep Learning Techniques for ECG Classification. Heart Disease Classification: Cardiac Arrhythmia Classification,    Heart Failure Classification, Cardiac Valve Stenosis Classification, Congenital Heart Disease Classification (CHD), Coronary Artery Disease Classification, Blood Pressure Hypertension Classification.