현재 이 사이트는 활발히 개발 중입니다. 문제를 발견하시면 신고해 주세요! - 문제 신고하기

INTRODUCTION TO ARTIFICIAL INTELLIGENCE

COMPSCI 540
과목 설명

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.

선수과목

(COMPSCI 300 , COMPSCI 320 or 367) and (MATH 211 , 217, MATH 221 , or 275) or graduate/professional standing or declared in the Capstone Certificate in Computer Sciences for Professionals

충족 요건

This course does not satisfy any prerequisites.

학점

미보고

개설 시기

미보고

평점
3.53

9.61% 과거 데이터 대비

수료율
99.46%

3.23% 과거 데이터 대비

A 비율
45.14%

29.97% 과거 데이터 대비

학급 규모
556

135.76% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

INTRODUCTION TO THEORY OF COMPUTING

COMPSCI 520

Basics about the notion, capabilities, and limitations of computation: elements of finite automata and regular languages, computability theory, and computational complexity theory. Additional topics include context-free grammars and languages, and complexity-theoretic cryptography.

ADVANCED NATURAL LANGUAGE PROCESSING

COMPSCI 769

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).

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.

GENERATIVE ARTIFICIAL INTELLIGENCE FOR ENGINEERING APPLICATIONS

EPD 522

Comprehensive coverage of AI-powered chatbots, from understanding generative AI fundamentals to developing sophisticated chatbot applications. Hands-on experience with generative AI tools. Explore retrieval-augmented generation (RAG) techniques. Examine critical aspects such as security, privacy, and memory models. Knowledge of Python [such asCOMP SCI 220orE P D 455] strongly recommended.

INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS

COMPSCI/ECE/ME 539

Theory and applications of artificial neural networks: multi-layer perceptron, self-organization mapdeep neural network convolutional neural network, recurrent network, support vector machines genetic algorithm, and evolution computing. Applications to control, pattern recognition, prediction, and object detection and tracking.