AI is going to bring major shifts in society through developments in self-driving cars, medical image analysis, better medical diagnosis, and personalized medicine. But for many, it still remains a mystery.
MOTIVATION
Each fall, we guide a cohort of members through our deep learning curriculum, complete with in-person workshops. Members will then have a chance to implement this content by working on a variety of team projects in the spring.
All of our curriculum resources (slide presentations, IPython notebooks, etc.) are publicly available for viewing in our Github. We recommend downloading and working through the incomplete notebooks from each lesson locally. You can then compare your work to the completed notebooks. We have also uploaded our full lectures onto our YouTube channel. We hope that these materials will serve as helpful resources for anyone wanting to learn more about deep learning and artificial intelligence.
(Note that deep learning/machine learning is only one part of the much larger field of artificial intelligence. If you are interested, we recommend you look into other topics in AI as well!)
OVERVIEW OF TOPICS
A brief overview of the most influential approaches to machine learning.
Sections
Part 1: k-Nearest Neighbors Part 2: Linear Regression Part 3: Logistic Regression Part 4: Support Vector Machines Part 5: Decision Trees Part 6: Naive BayesThis lesson discusses much of the surface level theory that goes into a neural network. From this theory, you should gain a good understanding of how basic neural networks work, which will then allow you to work on actual implementations.
Sections
Part 1: Architecture Part 2: Training Part 3: OptimizationThere are many advantages to varying the design of neural network architecture to suit a task's nature. In this lesson, we discuss common approaches to working with image and time-series data.
Sections
Part 1: Convolutional Neural Networks Part 2: Recurrent Neural Networks Part 3: Attention & Transformers Part 4: Generative Adversarial NetworksNEXT STEPS
We’re compiling a list of some exciting topics within machine learning (and AI in general) that you may want to check out. Note that this is not by any means a comprehensive list of advanced topics in AI/machine learning — this is simply a list of active research areas that we think you should know about, and may hopefully serve as a valuable starting point for branching out into more specific fields.
Sections
Part 1: Transfer Learning Part 2: Reinforcement LearningWe're compiling a list of interesting and influential papers that we recommend you check out, if you're interested.
CAIS++ has had the pleasure of inviting multiple speakers to talk about their research to our group. We list the information of past speakers and slides here.
