This course provides an overview of the field of natural language processing. Students will gain a thorough introduction to cutting-edge research in human language technology. Applications include machine translation, automatic summarization, question answering systems, and dialog systems.
Artificial intelligence (AI) holds tremendous promise to benefit nearly all aspects of society, including healthcare, food production, economy, education, security, the law, and even our personal activities. The development of AI is creating new opportunities to improve the lives of people around the world. This class aims to cultivate interdisciplinary learning. Students will be equipped with the technical and intellectual tools, ethical foundation, and psychological framework to successfully navigate the responsible AI practices. We will discuss various issues and concerns surrounding AI, such as ethics, fairness, and interpretability. Students are required to demonstrate AI for good in action with a mini-project and write a critique on current codes of ethics for the machine learning community.
One of the main aspirations of AI is to design intelligent systems that can communicate with people naturally and effectively. Designing such systems poses challenges that encompass multiple academic disciplines. In this course, we will discuss recent advances that bring together tools and theories from natural language processing, computer vision, robotics, linguistics, and spatial and social cognition to address these challenges.
In the first half of the class, we will discuss computational models of discourse with real-world applications, machine learning approaches for analyzing and modeling multimodal web data, and crowdsourcing for creating robust and interpretable systems. In the second half, we will study how we can extend these models and practices to model language generation, reasoning, and cooperation in interactive systems. Finally, we will discuss open problems in conversational AI, common sense inference and human-robot collaboration.
Intro to Statistics and Probability @ San Diego Mesa College
This course covers descriptive and inferential statistics. The descriptive portion analyzes data through graphs, measures of central tendency and dispersion. The inferential statistics portion covers statistical rules to compute the basic probability, including binomial, normal, Chi-squares, and T-distributions. This course also covers the estimation of population parameters, hypothesis testing, linear regression, correlation, and ANOVA. Emphasis is placed on applications of technology, using software packages, for statistical analysis and interpretation of statistical values based on data from disciplines including business, social sciences, psychology, life science, health science, and education. This course is intended for transfer students interested in statistical analysis.
Statistics for MBA @ San Diego State
This course provides the fundamental methods of statistical analysis, art and science of extracting information from data. The course will begin with a focus on the basic elements of exploratory data analysis, probability theory, and statistical inference. With this as a foundation, it will proceed to explore the use of the key statistical methodology known as regression analysis for solving business problems, such as the prediction of future sales and the response of the market to price changes.