Course outline:

AI basics

AI practicalities: Using Llama, ChatGPT, Copilot, Bing, Claude, Gemini and others.

        AI inference engine installation

 AI and text

        Generation

        Digestion

        Hallucination

        Citation

        Literature surveys

        Legalities

 AI and code

        Generation

                Code graphics, analysis, and visualization

        Checking

        Garbage in, garbage out.

 Prompt engineering

 Making a web page with AI

 AI and graphics

        Scientific illustration

        Image manipulation. Fakes.

        Is it art?

 AI and sound

        Sound manipulation basics

        Speech synthesis

        Music. Is it art?

AI and science

        Alpha fold

        DeepLabCut and SLEAP

        Perch

                Classifiers

        Is it science?

Build an AI

        Train it

        Validate it

        Break it

        Inference engine

 AI concepts and where they fit in training, deploying, and generating.

Engrams

Perceptrons

RNNs

CNNs

Diffusion

Generator

Generative adversarial networks

Variational autoencoders

LSTM

Foundation models and LLMs

Embedding

Transformers and GPTs

Reading

AI for beginners: https://microsoft.github.io/AI-For-Beginners/

Dmitry Soshnikov.

http://www.bioinf.jku.at/publications/older/2604.pdf

Hochreiter and Schmidhuber 1997.

https://colah.github.io/posts/2015-08-Understanding-LSTMs/

https://fullstackdeeplearning.com/course/2022/lecture-7-foundation-models

Attention is all you need: Vaswani et al 2017

Course outcomes:

Understand basic AI principles

Use AIs effectively for academic and research purposes

Be familiar with flaws, limitations, ethical and legal drawbacks of AIs