CIS1875 - NATURAL LANGUAGE PROCESSING
3 Credit Hours
Student Level:
This course is open to students on the college level in either the Freshman or Sophomore year.
Catalog Description:
CIS1875 - Natural Language Processing (3 hrs.)
This course explores AI-driven techniques for understanding, processing, and generating human language. Students will gain both theoretical and practical expertise in language representation, comprehension, and generation.
Course Classification: Lecture
Prerequisites:
None
Co-requisites:
None
Controlling Purpose:
This course provides students with a comprehensive understanding of Natural Language Processing (NLP), a branch of artificial intelligence focused on the interaction between computers and human language. Students will explore both the theoretical principles and practical applications of NLP, developing expertise in language representation, comprehension, and generation.
Learner Outcomes:
Upon completion of the course, the student will:
- Understand the Fundamentals of NLP: Students will explain the core concepts, historical development, and real-world applications of Natural Language Processing (NLP), while considering the ethical implications of its use.
- Apply Language Representation Techniques: Students will utilize tokenization, text preprocessing, and word embeddings to represent language, and analyze how syntax and semantics contribute to effective language representation.
- Analyze Language Understanding Methods: Students will perform language understanding tasks, including part-of-speech tagging, named entity recognition, sentiment analysis, document classification, information extraction, and text summarization.
- Demonstrate Language Generation Skills: Students will apply various text generation techniques to create coherent and contextually relevant outputs, including dialogue systems, chatbots, and creative language generation.
- Explore Advanced NLP Topics: Students will investigate cutting-edge NLP topics such as neural network architectures, transfer learning, and multimodal NLP to understand their impact on language processing tasks.
- Integrate NLP Techniques into Practical Applications: Students will combine language representation, understanding, and generation methods to design and implement solutions for real-world NLP challenges.
Unit Outcomes for Criterion Based Evaluation:
The following outline defines the minimum core content not including the final examination period. Instructors may add other material as time allows.
UNIT 1: Understanding the Fundamentals of NLP
Outcomes: Upon completion of this unit, students will be able to:
- Explain the core concepts and historical development of Natural Language Processing (NLP).
- Identify real-world applications of NLP in fields such as healthcare, finance, and customer service.
- Discuss the ethical implications of NLP, including bias, privacy concerns, and responsible AI use.
UNIT 2: Applying Language Representation Techniques
Outcomes: Upon completion of this unit, students will be able to:
- Implement text preprocessing techniques such as tokenization, stemming, lemmatization, and stopword removal.
- Utilize word embeddings, such as Word2Vec, GloVe, and contextualized embeddings, to represent language.
- Analyze the role of syntax and semantics in effective language representation.
UNIT 3: Analyzing Language Understanding Methods
Outcomes: Upon completion of this unit, students will be able to:
- Perform part-of-speech tagging, named entity recognition, and sentiment analysis.
- Implement document classification, information extraction, and text summarization techniques.
- Evaluate different NLP models for language understanding tasks based on accuracy and efficiency.
UNIT 4: Demonstrating Language Generation Skills
Outcomes: Upon completion of this unit, students will be able to:
- Apply text generation techniques, including rule-based methods, statistical approaches, and neural networks.
- Develop dialogue systems and chatbots that generate coherent and contextually relevant responses.
- Experiment with creative language generation methods, such as poetry and story generation using NLP models.
UNIT 5: Exploring Advanced NLP Topics
Outcomes: Upon completion of this unit, students will be able to:
- Investigate neural network architectures used in NLP, such as transformers and recurrent neural networks.
- Explore transfer learning techniques and pre-trained models like BERT, GPT, and T5.
- Analyze multimodal NLP applications that integrate text, speech, and visual data.
UNIT 6: Integrating NLP Techniques into Practical Applications
Outcomes: Upon completion of this unit, students will be able to:
- Combine language representation, understanding, and generation techniques to solve real-world problems.
- Design and implement NLP applications such as chatbots, sentiment analysis tools, or text summarizers.
- Evaluate the performance and limitations of NLP-based solutions in practical scenarios.
Projects Required:
Varies, refer to syllabus.
Textbook:
Contact Bookstore for current textbook.
Materials/Equipment Required:
None
Attendance Policy:
Students should adhere to the attendance policy outlined by the instructor in the course syllabus.
Grading Policy:
The grading policy will be outlined by the instructor in the course syllabus.
Maximum class size:
Based on classroom occupancy
Course Time Frame:
The U.S. Department of Education, Higher Learning Commission and the Kansas Board of Regents define credit hour and have specific regulations that the college must follow when developing, teaching and assessing the educational aspects of the college. A credit hour is an amount of work represented in intended learning outcomes and verified by evidence of student achievement that is an institutionally-established equivalency that reasonably approximates not less than one hour of classroom or direct faculty instruction and a minimum of two hours of out-of-class student work for approximately fifteen weeks for one semester hour of credit or an equivalent amount of work over a different amount of time. The number of semester hours of credit allowed for each distance education or blended hybrid courses shall be assigned by the college based on the amount of time needed to achieve the same course outcomes in a purely face-to-face format.
Refer to the following policies:
402.00 Academic Code of Conduct
263.00 Student Appeal of Course Grades
403.00 Student Code of Conduct
Accessibility Services Program:
Cowley College, in recognition of state and federal laws, accommodates all students with a documented disability. If a student has a disability that will impact their ability to be successful in this course, please contact the Student Accessibility Coordinator for the needed accommodations.
DISCLAIMER: THIS INFORMATION IS SUBJECT TO CHANGE. FOR THE OFFICIAL COURSE PROCEDURE CONTACT ACADEMIC AFFAIRS.
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