Description automatically generated with medium confidence


Spring 2023 MW 4:30pm 5:50pm, April 3rd June 5th

Location: Gates B12


Edward Chang (echang@cs.stanford.edu)

Adjunct Professor, Computer Science, Stanford University


CA: Henrik Marklund


Office Hours: MW after class to 6:15pm at Gates B12 or 104.








The 2023 edition of the course will focus on topics related to Generative AI, including GPT-3/4, ChatGPT, BioGPT, and consciousness modeling.


Artificial intelligence, specifically deep learning, attention mechanisms, and foundation models, has emerged as some of the most transformative technologies of the past decade. AI can already outperform humans in various computer vision and natural language processing tasks. However, we still face some limitations and obstacles reminiscent of those that contributed to the decline of the first AI boom five decades ago. This research-oriented course will initially examine the limitations (e.g., iid assumption on training and testing data, extensive training data requirements, and lack of interpretability) of widely used AI algorithms, including convolutional neural networks (CNNs), transformers, reinforcement learning, generative AI, LLMs, and AI safety. To address these limitations, we will explore topics such as transfer learning to alleviate data scarcity, knowledge-guided multimodal learning to enhance data diversity, out-of-distribution generalization, subspace learning for interpretability, privacy-preserving data management, emotion/behavior/ethics modeling, and more.

The course will be taught through a combination of lectures and project sessions. Guest speakers from academia and industry will present lectures on specialized AI applications (e.g., cancer/depression diagnosis and treatment). Students will be assigned to work on a term project relevant to their fields of study (e.g., CS, Medicine, and Data Science). Projects may involve conducting literature surveys, formulating ideas, and implementing these ideas. Students are encouraged to develop a project that integrates their own graduate research.


Note: The following prerequisite for taking this course have been waved because of the ease-of-use of ChatGPT. 

Level: Senior/graduate of EE/CS, Medicine, Medical Informatics, Engineering; proficient in basic programming

Perquisite: Introductory course in AI, Statistics, or Machine Learning




·   No exams.

·   Three light-weight assignments, using ChatGPT to complete.

·   Term project: A group project of two to three involving prompt template design. 


References (optional)


Probabilistic Machine Learning, Kevin P. Murphy, March 2022

Projections: A Story of Human Emotion, Karl Deisseroth, 2021




·   Assignments 30%

·   Class participation 10%

·   Literature survey and project proposal 20%

·   Project implementation and demo 40%


  Syllabus (last updated 4/03/2023)




Course Materials


Week #1



Course Aims and Syllabus

ChatGPT Intro


Intro to AI, GAI and why you should or should not take this course.

E. Chang


LLMs (Large Language Models) Part 1 of 4

Foundation Models and ChatGPT


Intro to GPT-3, GPT-4, ChatGPT, BioGPT and Prompt engineering.

[1]  J. Wei, X. Wang, et al, Chain of thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 2022. [link]

[2]  J. Jung, L. Qin, S. Welleck, F. Brahman, C. Bhagavatula, R. L. Bras, and Y. Choi. Maieutic prompting: Logically consistent reasoning with recursive explanations. In Conference on Empirical Methods in Natural Language Processing, 2022. [link]

[3] Prompting Large Language Models with the Socratic Method, E. Y. Chang, IEEE CCWC, March 2023. [link]

[4] Sparks of Artificial General Intelligence: Early experiments with GPT-4,

Sébastien BubeckVarun Chandrasekaran, et al, March 2003. [link]


E. Chang


Assignment #1: Design [your] chatbot functions, using ChatGPT


Henriks [PPT]


Week #2


P4 Medicine, Part 1 of 3:

History of AI in Diagnosis [link]


[1] Schwartz, W. B., R. S. Patil, and P. Szolovits. "AI in Medicine: where do we stand." New England Journal of Medicine 316 (1987): 685-688. [link].

[2] Wu, T. D. "Efficient Diagnosis of Multiple Disorders Based on a Symptom Clustering Approach." Proceedings of AAAI, 1990, pp. 357-364.

[3] Universal Equivariant Multilayer Perceptrons, Siamak Ravanbakhsh, ICML, 2020.

[4] Tricorder (medical IoTs), E. Y. Chang, et al., "Artificial Intelligence in XPRIZE DeepQ Tricorder. " ACM MM Workshop for Personal Health and Health Care, 2017.

[5] Szolovits, P., and S. G. Pauker. "Categorical and Probabilistic Reasoning in Medical Diagnosis. Artificial Intelligence 11(1-2), 1978: 115-144.

[6] Patil, R. S., P. Szolovits, and W. B. Schwartz. "Causal Understanding of Patient Illness in Medical Diagnosis." In Proceedings of the Seventh International Joint Conference on Artificial Intelligence.


E. Chang


LLM, Part 2 of 4:

Prompt template design principles [link]

+ Noora introduction



[1] Prompting Large Language Models with the Socratic Method, E. Y. Chang, IEEE CCWC, March 2023. [link]

[2] CRIT: Critical Reading Inquisitive Template, E. Y. Chang. [link]

[3] Noora.cs.stanford.edu


E. Chang


Assignment #1 due

Assignment #2: Design and test prompting templates

Week #3


LLM, Part 2 of 4 finish the lecture

Prompt template design principles

+ project discussion


[1] Prompting Large Language Models with the Socratic Method, E. Y. Chang, IEEE CCWC, March 2023. [link]

[2] CRIT: Critical Reading Inquisitive Template, E. Y. Chang. [link]

[3] Noora.cs.stanford.edu


E. Chang


LLM, Part 3 of 4:

History of NLP in one lecture.

From one-hot vector, word2vec to attention, transformers, BERT, and GPT [slides][video]


[1] Attention is all you need, Ashish Vaswani, et al., [link].

[2] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding [link].

[3] Reformer: The Efficient Transformer [link].

[4] Perceiver: General Perception with Iterative Attention [link].


E. Chang


Assignment #2 due

Assignment #3: Design chatbot states

Week #4


Intro to Psychiatric Disorders, symptoms, and treatments [slides][video]





Dr. Vikas Duvvuri,

MD/PhD Stanford


ChatGPT for Better Communication?  Envisioning the Future of Speech Language Therapy via Generative AI


[1] Lee, S. A. S. (2019). Virtual speech-language therapy for individuals with communication disorders: Current evidence, limitations, and benefits. Current Developmental Disorders Reports6, 119-125.

[2] Du, Y., Grace, T. D., Jagannath, K., & Salen-Tekinbas, K. (2021). Connected play in virtual worlds: communication and control mechanisms in virtual worlds for children and adolescents. Multimodal Technologies and Interaction5(5), 27.

[3] Edwards-Gaither, L., Harris, O., & Perry, V. (2023). Viewpoint Telepractice 2025: Exploring Telepractice Service Delivery During COVID-19 and beyond. Perspectives of the ASHA Special Interest Groups, 1-6.


Assignment #3 due


Prof. Yao Do, Medical School, USC


Week #5


P4 Medicine, Part 2 of 3:

Healthcare with Small Data



Part#2 Training Your Own ChatGPT [slides]

[1] Daphne Koller:  Biomedicine and Machine Learning, AI Podcast #93 with Lex Fridman, May 2020 [link]

[2] Edward Y. Chang et al., "Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning." AAAI (2018).

[3] Edward Y. Chang et al., "REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis." NIPS (2018).


E. Chang



Project proposal and related work presentation



Henrik Marklund

E. Chang

Week #6


P4 Medicine, Part 3 of 3:

Frontier Research [slides]

(Healthcare is already in the 3rd AI winter due to data and regulations.)

[1] On Bottleneck of Graph Neural Networks and its Practical Implications, Uri Alon, Eran Yahav, ICRL 2021.

[2] Panel: VR/AR for Surgery and Medical Education, April 2018 [link].

[3] Stanford SNI Talk: Advancing Healthcare w/ AI and VR, Edward Y. Chang [link] (Stanford ID required).

[4] The problem of Protein Folding, Wikipedia.



E. Chang


LLM Part 4 of 4:

Virtual Assistant and Augmented LM. [slides]

[1] Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands
G. Campagna, S. Xu, M. Moradshahi, R. Socher, and Monica S. Lam
In Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, Phoenix, AZ, June 2019 [link].

[2] Augmented Language Model, a survey, Meta, 2023 [link].


E. Chang


Week #7


Consciousness & Mind, Part 1 of 3: What is Consciousness?

Rule of Nature in Philosophy and Physics (lecture #14) [slides]


[1] What is Life, Erwin Schrodinger, online book [link].

[2] COCOMO: Computational Consciousness Modeling, E.Y. Chang, 2023 [link].


E. Chang



Consciousness & Mind, Part 2 of 3: Computational Consciousness, COCOMO (lecture #15) [slides].

[1] Projections, by Karl Deisseroth, Random House, 2021.

[2] Discovery of a Perceptual Distance Function for Measuring Image Similarity, Beitao Li, Edward Chang, and Yi Wu, Multim. Syst., 2003 [link].

[3] COCOMO: Computational Consciousness Modeling, E.Y. Chang, 2023 [link].



E. Chang


Week #8


Cancer, Part 1 od 2: Cancer Causes and Diagnosis (lecture #12)


[1] Principles and methods of integrative genomic analyses in cancer, Vessela N. Kristensen, Ole Christian Lingjærde, Hege G. Russnes, Hans Kristian M. Vollan, et al., Nature Reviews, 2014.

[2] Biomarker development in the precision medicine era: lung cancer as a case study, Ashley J. Vargas, and Curtis C. Harris, Natural Reviews, 2016.

[optional] Artificial intelligence in radiology, Ahmed Hosny, Chintan Parmar, John Quackenbush, Lawrence H. Schwartz,and Hugo J. W. L. Aerts, Nature Reviews, 2018.


Dr. Melissa Ko,

PhD, Cancer Biology,



E. Chang


Cancer, Part 2 of 2: Cancer Treatment and How AI May Help (lecture #13) [video][Slides]

[1] Mass cytometry: blessed with the curse of dimensionality, Evan W Newell & Yang Cheng, Nature Immunology, June 2016.

[2] Next-Generation Machine Learning for Biological Networks, Diogo M. Camacho, Katherine M. Collins, Rani K. Powers, James C. Costello, and James J. Collins, Leading Edge Review, June 2018.

[optional] Personalized Cancer Models for Target, Discovery and Precision Medicine, Carla Grandori1 and Christopher J. Kemp, Trends in Cancer, CellPress Reviews, September 2018.


Dr. Melissa Ko,

PhD, Cancer Biology,



Henrik Marklund

Week #9



Memorial Holiday




Consciousness & Mind, Part 3 of 3: GAI Alignment, Free Will, Ethics, and Mind; Course Summary (lecture #16)


[1] COCOMO: Computational Consciousness Modeling, E.Y. Chang, 2023.


E. Chang



Project Presentation I

Please sign up (sending us email)

E. Chang

Henrik Marklund

Week #10

6/5 (last day of class)


Project Presentations II

Presentation time is flexible to arrange between 5/31 and 6/5.   If later than 6/5, then online presentation can be arranged.

E. Chang

Henrik Marklund