Deep learning for science students

Yonsei's eagle generated by a GAN

Animations of Yonsei's Eagle Statue generated by a GAN algorithm.

Note: the course was postponed for personal reasons.

Course Description

In recent years, scientist have found applications of Machine Learning/Deep Learning in areas as diverse as pure mathematics (knot theory) or medicine (protein structure prediction). The industry has also benefit from the use of Deep Learning to automatize their process and create new products. In this class, our main goal is to provide the tools that students (math or science students with strong analytical skills but not much programming experience) require to be able to run their own experiments.

Requirements: Linear algebra and multilinear calculus.

Bibliography

You can find this books for free online:

How to Think Like a Computer Scientist: Interactive Edition

Deep learning/Goodfellow, Ian/MIT Press/2017

Pattern Recognition and Machine Learning

Machine Learning For Humans

You can find this books at the library:

Deep Learning with Pytorch, First Edition

Deep Learning with Python, Second Edition

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition

Time & Place

Lecture: Monday, Wednesday, Friday 10:00AM - 10:50PM Room 262 Yonsei's math department

Instructors, Office Hours, Online help

Eric Dolores Cuenca

TA TBD

Policies

Grading

100pt for class project

100pt for group project

100pt for homeworks / presentations

Late Policy

If needed, the deadline of an assignment can be extended up to one week after the initial deadline with no evidence required from the student. Just send an email requestig the extension.

Cheating & Academic Dishonesty

If you have a question about whether something may be considered cheating, ask, prior to submitting your work.

Attendance

Attendance is not graded.

(Tentative) Course Calendar

주	기간	수업내용	교재범위,과제물	비고
March/2-March3	Introduction	What is Machine Learning? Python review: Genetic Algorithms. Jupyter Notebooks
March/7-March11	Data Oriented Machine Learning.	The data pipeline. Neural networks. Pytorch vs Keras. Tensorboard.		Course add and drop on March/8. March/9 Holiday.
March/14-March18	Data Oriented Machine Learning.	Optimization. Back propagation. Lightning.
March/21-March25	Data Oriented Machine Learning.	Regularization. Double descent. Testing.		Friendly competition between Yonsei University Math Department and ESFM (Escuela Superior de Física y Matemáticas), Mexico
March/28-April1	Deep learning for images.	CNN. Cloud computing.
April/4-April/8	Deep learning for images.	CNN. Pooling. Docker.
April/11-April/15	Class project plan.
April/18-April/22	Class project.			Midterm examination period April/20-april/26
April/25-April/29	Class project report. Social Bias.	Social biases and technology. Multiprocessing library.		Midterm examination period April/20-april/26
May/2-May/6	Generative deep learning.	GANS. Pix2pix. Profiling code.		May/5 Holiday
May/9-May/13	Deep learning for text.	Recurrent NN, LSTM. Gradio vs Streamlit, huggingface, autoML.
May/16-May/20	Deep learning for text.	N-grams, Bag of words. Final group project check.
May/23-May/27	Transformers.	BERT and GPT. Final group project check.
May/30-June/3	Deep learning for time series.	Final group project check. Text to Image: VQGaN+CLIP, Disco Diffusion, DALL-E2, Google Imagen, Parti, Craiyon, Cogview2		June/1 Holiday
June/6-June/10	Final group project check.	Final group project check.		June/6 Holiday June/8—June/14 supplementary lessons
June/13-June/17	Final group project submission.			June/8—June/14 supplementary lessons June/15-June/21 final examination period.
June/20-June/24				June/22 Summer Vacation.

List of papers to read: