Artificial Intelligence for Beginners – A Curriculum

Artificial Intelligence for Beginners

Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about Artificial Intelligence.

In this curriculum, you will learn:

  1. Different approaches to Artificial Intelligence, including the “good old” symbolic approach with Knowledge Representation and reasoning (GOFAI).
  2. Neural Networks and Deep Learning, which are at the core of modern AI. We will illustrate the concepts behind these important topics using code in two of the most popular frameworks - TensorFlow and PyTorch.
  3. Neural Architectures for working with images and text. We will cover recent models but may lack a little bit on the state-of-the-art.
  4. Less popular AI approaches, such as Genetic Algorithms and Multi-Agent Systems.

What we will not cover in this curriculum:

IIntroduction to AI
1Introduction and History of AIText
IISymbolic AI
2Knowledge Representation and Expert SystemsTextExpert System, Ontology, Concept Graph
IIIIntroduction to Neural Networks
4Multi-Layered Perceptron and Creating our own FrameworkTextNotebookLab
5Intro to Frameworks (PyTorch/TensorFlow) and OverfittingTextPyTorchKeras/TensorFlowLab
IVComputer VisionMicrosoft Azure AI Fundamentals: Explore Computer Vision
Microsoft Learn Module on Computer VisionPyTorchTensorFlow
6Intro to Computer Vision. OpenCVTextNotebookLab
7Convolutional Neural Networks
CNN Architectures
8Pre-trained Networks and Transfer Learning
Training Tricks
Dropout sample
Adversarial Cat
9Autoencoders and VAEsTextPyTorchTensorFlow
10Generative Adversarial Networks
Artistic Style Transfer
TextPyTorchTensorFlow GAN
Style Transfer
11Object DetectionTextPyTorchTensorFlowLab
12Semantic Segmentation. U-NetTextPyTorchTensorFlow
VNatural Language ProcessingMicrosoft Azure AI Fundamentals: Explore Natural Language Processing
Microsoft Learn Module on Natural language processingPyTorchTensorFlow
13Text Representation. Bow/TF-IDFTextPyTorchTensorFlow
14Semantic word embeddings. Word2Vec and GloVeTextPyTorchTensorFlow
15Language Modeling. Training your own embeddingsTextPyTorchTensorFlowLab
16Recurrent Neural NetworksTextPyTorchTensorFlow
17Generative Recurrent NetworksTextPyTorchTensorFlowLab
18Transformers. BERT.TextPyTorchTensorFlow
19Named Entity RecognitionTextTensorFlowLab
20Large Language Models, Prompt Programming and Few-Shot TasksTextPyTorch
VIOther AI Techniques
21Genetic AlgorithmsTextNotebook
22Deep Reinforcement LearningTextPyTorchTensorFlowLab
23Multi-Agent SystemsText
VIIAI Ethics
24AI Ethics and Responsible AITextMS Learn: Responsible AI Principles
X1Multi-Modal Networks, CLIP and VQGANTextNotebook

Each lesson contains some pre-reading material (linked as Text above), and some executable Jupyter Notebooks, which are often specific to the framework (PyTorch or TensorFlow). The executable notebook also contains a lot of theoretical material, so to understand the topic you need to go through at least one version of the notebooks (either PyTorch or TensorFlow). There are also Labs available for some topics, which give you an opportunity to try applying the material you have learned to a specific problem.

Some sections also contain links to MS Learn modules that cover related topics. Microsoft Learn provides a convenient GPU-enabled learning environment, although in terms of content you can expect this curriculum to go a bit deeper.


✍️ Primary Author: Dmitry Soshnikov, PhD <br/> 🔥 Editor: Jen Looper, PhD <br/> 🎨 Sketchnote illustrator: Tomomi Imura <br/> ✅ Quiz Creator: Lateefah Bello, MLSA <br/> 🙏 Core Contributors: Evgenii Pishchik