This course provides an in-depth exploration of Artificial Intelligence technologies, with a particular focus on advancements in Natural Language Processing (NLP) and Generative AI. Delve into renowned AI models like GPT, BERT, and their applications, gaining hands-on experience with powerful tools like TensorFlow, Keras, and Hugging Face.
Throughout the course, you'll gain a solid understanding of NLP fundamentals and learn about the diverse range of technologies and products developed using NLP and Generative AI. This includes a comprehensive study of how these technologies are revolutionizing text processing and generation. Key highlights of the course include practical sessions on utilizing pre-trained Large Language Models (LLMs), where you'll learn how to use their APIs for various applications. You'll also acquire skills in fine-tuning LLMs for specialized tasks, and gain insights into the methodologies for retraining these models to suit specific requirements.
There are weekly assignments, which will improve both your theoretical understanding and your practical skills. All assignments contain both written questions and programming parts. Only top 13 assignments will be considered.
We are committed to doing what we can to work for equity and to create an inclusive learning environment that actively values the diversity of backgrounds, identities, and experiences of everyone in TAC 459. We also know that we will sometimes make missteps. If you notice some way that we could do better, we hope that you will let someone in the course staff know about it.
Students are required to independently submit their solutions for TAC 459 homework assignments. Collaboration with generative AI tools such as Co-Pilot and ChatGPT is allowed, treating them as collaborators in the problem-solving process. However, the direct solicitation of answers or copying solutions, whether from peers or external sources, is strictly prohibited.
| Course Assistant | Mode | Location | Day | Time Slot | Meeting Link | |
|---|---|---|---|---|---|---|
| Gaurav Tadkapally | tadkapal@usc.edu | In Person + Online | TBD | Monday | 11:00am - 12:00pm | meet.google.com/fid-azra-hsg |
| Rajeev Singh | rajeevdh@usc.edu | Online | Google Meet | Thursday | 02:00pm - 03:00pm | meet.google.com/oqy-wueg-twt |
| Rajeev Singh | rajeevdh@usc.edu | In Person + Online | OHE Patio | Monday | 12:30pm - 01:30pm | meet.google.com/oqy-wueg-twt |
Updated lecture slides will be posted on Blackboard before each lecture.
| Date | Description | Assignments |
|---|---|---|
| Week 1 Mon Jan 8 |
Class Introduction
Intro to NLP and AI
|
|
| Week 2 Mon Jan 15 |
Holiday
|
Assignment 1
Deadline: 1/16 |
| Week 3 Mon Jan 22 |
Fundamentals of Machine Learning for NLP
|
Assignment 2
Deadline: (Extended to 1/29) |
| Week 4 Mon Jan 29 |
Neural Networks and Deep
Learning in NLP
|
Assignment 3
Deadline: 1/29 |
| Week 5 Mon Feb 5 |
Transformer Models and
Attention Mechanisms
|
Assignment 4
Deadline: 2/05 |
| Week 6 Mon Feb 12 |
Syntax, Parsing, Word
Embeddings, and POS Tagging
|
Assignment 5
Deadline: 2/12 |
| Week 7 Mon Feb 19 |
Holiday
|
Assignment 6
Deadline: 2/20 |
| Week 8 Mon Feb 26 |
Text Classification & Machine
Translation + Midterm Exam
|
Assignment 7
Deadline: 2/27 |
| Week 9 Mon Mar 4 |
Advanced Topics in Machine
Learning and NLP
|
Assignment 8
Deadline: 2/26 |
| Mon Mar 11 | Spring Break | |
| Week 10 Mon Mar 18 |
Named Entity Recognition,
Information Retrieval and Search
|
Assignment 9
Deadline: 3/18 |
| Week 11 Mon Mar 25 |
Advanced Machine Translation
and Summarization
|
Assignment 10
Deadline: 3/25 |
| Week 12 Mon Apr 01 |
Speech Processing and
Conversational AI
|
Assignment 11
Deadline: 4/01 |
| Week 13 Mon Apr 08 |
Generative AI, Ethical
Considerations, and NLP APIs
|
Assignment 12
Deadline: 4/08 |
| Week 14 Mon Apr 15 |
Fine-Tuning LLMs, Hugging Face,
LangChain
|
Assignment 13
Deadline: 4/15 |
| Week 15 Mon Apr 22 |
Course Review, Future Trends + Final Exam
|
Assignment 14
Deadline: 4/23 |
| Mon Apr 29 | Study Day | |
| Week 16 Mon May 06 |
Final Project Presentations
|
