Schedule
Week 0: Course Overview, Prerequisite Knowledge
Purpose
- Gen acquainted with the course
- Learn some of the pre-reqs necessary (Python)
- Learn some of the pre-reqs necessary (calculus, linear algebra)
Resources
Python Resources:
Math Resources:
- Linear Algebra Review and Reference (Just Unit 1)
- Vector Basics
- Hyperplane Definition
- Hyperplane Math
- Partial Derivatives (Mathematical)
- Partial Derivatives (Graphical)
Assignment
IMPORTANT!: If you already know a topic, DO NOT waste your time re-doing it. This week is just to refresh your mind
That being said, if you are unfamiliar with most of the items here, focus on Python and be sure to ask lots of questions!
Python Assignments:
- Complete units in the CS50x Intro to Python Course as needed (If you are confident, feel free to skip a section). I recommend:
- 0: Functions, Variables
- 1: Conditionals
- 2: Loops
- 4: Libraries
- 9: Et Cetera
- Read through CS231n Notes, focusing on:
- Containers
- Numpy
- MatPlotLib
Math Assignments:
- Read/Watch provided resources to learn/refresh your math knowledge.
- If you are already familiar with the concepts listed above, feel free to skip them. I would, however, reccomend you explore linear algebra in high dimensional spaces (specifically hyperplanes) and revisit partial derivatives.
Summary Questions
- What topics did you learn this week (Python or math)?
Week 1: Introduction to Machine Learning
Purpose
An introduction to the foundation of modern AI!
- Introduction to Machine Learning (ML)
- Math behind Linear Regression, SVM, PCA
- Basic Unsupervised Clustering
Resources
Assignment
- Complete Unit 1 in the megadoc (synthesis questions included)
- Either Scikit-Learn ML Technical-Project (in megadoc)
- Or Non-Technical Writing Project (bottom of megadoc)
Summary Questions
- Synthesis questions in the megadoc
Week 2: Introduction to Neural Networks
Purpose
Our first foray into more complex networks and how they learn.
- Introduction to Deep Learning (DL)
- Introduction to Neural Networks
- Introduction to Backpropagation
Resources
- Megadoc Unit 2
- CS231n Notes on Neural Networks. See Module 1: Neural Networks - great written resource for the basics.
Assignment
- Complete Unit 2 in the megadoc (synthesis questions included)
- Basic MNIST Classifier (in megadoc/github)
Summary Questions
- Synthesis questions in the megadoc
Week 3: Basic Neuroanatomy
Purpose
Welcome to a beginner’s introduction to neuroscience! We will
- Learn several basic regions of the brain
- Learn fundamentals of the neuron and biological computation -Begin to hypothesize about the parallels and divergences of machine learning and the brain
Resources
Assignment
- Complete Unit 3 in the megadoc (synthesis questions included)
- Basic Neuroanatomy Project (LaTeX template in github)
Summary Questions
- Synthesis questions in the megadoc
Week 4: Convolutional Neural Networks
Purpose
- Deep Learning for Vision
- Learn about Convolutional Neural Networks
Resources
Assignment
- Complete Unit 4 in the megadoc (synthesis questions included)
- ConvNet Project (in megadoc/github)
Summary Questions
- Synthesis questions in the megadoc
Week 5: The Visual System
Purpose
This week we take a small break from computation and return to the brain, specifically your insanely complex and elegant visual system! We will
- Learn the basic anatomical and functional regions of the visual system
- Compare biological solutions to visual tasks with computational solutions
Resources
Week 6: Reinforcement Learning
Purpose
- Go over basics of RL
- Learn about Deep Q Learning
Resources
Assignment
- Complete Unit 6 in the megadoc (synthesis questions included)
Summary Questions
- Synthesis questions in the megadoc
- What’s one resource that was helpful (suggested or found on your own)?
Week 7: AI Ethics
Purpose
- Learn about ethics in AI and think about how ethical AI is developed.
Resources
Assignment
- Create a slideshow, details in the megadoc
Summary Questions
- Synthesis questions in the megadoc
Week 8: Language Modeling
Purpose
- Learn about word embeddings
- Learn about Recurrent Neural Networks
- Learn about Transformers
- Learn about fine-tuning foundation models like GPT
- Gain a basic intuition about transformer and language models
Resources
Assignment
- Complete Unit 8 in the megadoc (synthesis questions included)
- HuggingFace Project Part 1 (in megadoc)
- HuggingFace Project Part 2 (in megadoc)
Summary Questions
- Synthesis questions in the megadoc