ADVANCED NATURAL LANGUAGE PROCESSING

Name: ADVANCED NATURAL LANGUAGE PROCESSING
Availability: OutOfStock
Rating: 4.7 (336 reviews)

COMPSCI 769

과목 설명

Develop algorithms and mathematical models for natural language processing tasks, including text categorization, information retrieval, speech recognition, machine translation, and information extraction. Focus is on the state-of-the-art computational techniques as they are applied to natural language tasks. Students are strongly encouraged to have knowledge of introductory artificial intelligence (e.g.,COMP SCI 540).

선수과목

Graduate/professional standing

충족 요건

This course does not satisfy any prerequisites.

학점

미보고

개설 시기

미보고

평점

3.7

-1.17% 과거 데이터 대비

수료율

100%

0.3% 과거 데이터 대비

A 비율

57.89%

-15.42% 과거 데이터 대비

학급 규모

13.1% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

INTRODUCTION TO TEXT MINING

LIS 501

Introduces computational methods and tools for processing, analyzing, and understanding text data. Topics include text data preparation and preprocessing, models of text content and meaning, exploratory text analytics, text classification, information extraction from texts, ethical issues in natural language processing (NLP), and related applications in information sciences and other fields. Develops practical skills to design and implement text mining solutions using popular NLP tools and programming packages.

TEXT ANALYTICS AND BUSINESS APPLICATION

INFOSYS 723

An introduction to text mining and natural language processing for business applications. Provides an overview of text data and steps to make it usable and approaches for making text data useful in descriptive and predictive analytics applications. Topics include representation approaches, topic modeling, and an overview of key applications of natural language processing, such as chatbots and recommender systems.

INTRODUCTION TO ARTIFICIAL INTELLIGENCE

COMPSCI 540

Principles of knowledge-based search techniques, automatic deduction, knowledge representation using predicate logic, machine learning, probabilistic reasoning. Applications in tasks such as problem solving, data mining, game playing, natural language understanding, computer vision, speech recognition, and robotics.

MACHINE LEARNING

COMPSCI/ECE 760

Computational approaches to learning: including inductive inference, explanation-based learning, analogical learning, connectionism, and formal models. What it means to learn. Algorithms for learning. Comparison and evaluation of learning algorithms. Cognitive modeling and relevant psychological results.

MATHEMATICAL FOUNDATIONS OF MACHINE LEARNING

COMPSCI/ECE 761

Mathematical foundations of machine learning theory and algorithms. Probabilistic, algebraic, and geometric models and representations of data, mathematical analysis of state-of-the-art learning algorithms and optimization methods, and applications of machine learning. Knowledge of probability [such asMATH/STAT 431orCOMP SCI/E C E 561] and linear algebra [such asMATH 341orM E/COMP SCI/E C E 532] is required.