Week 1
Lesson 1
Week 1: Foundations of AI and Data
In this introductory week, students will gain a broad understanding of Artificial Intelligence and its significance in today's world. We'll explore various types of AI and their applications. The second half of the week focuses on the fundamentals of data analysis. Students will learn to use Python and the pandas library to manipulate and analyze datasets. The week concludes with a hands-on lab where students will explore a real-world dataset and create their data visualizations.
 Week 2
Lesson 2
Week 2: Data Visualization and Data Processing
Building on the previous week, students will dive deeper into data visualization techniques. They'll learn to use matplotlib and seaborn libraries to create various types of plots and charts, including scatter plots, line charts, and heatmaps. The latter part of the week introduces the concept of Neural Networks. Students will learn about neurons, layers, and basic network architectures. They'll visualize simple neural networks and complete a mini-project creating an infographic.
 Week 3
Lesson 3
Week 3: Natural Language Processing Basics
This week focuses on the foundations of Natural Language Processing (NLP). Students will learn about the challenges of processing human language and the basic techniques used to prepare text data for analysis. Topics include tokenization, stemming, and lemmatization. Students will practice visualizing text data using word clouds and frequency plots. The week culminates in a lab where students build a simple text classifier using basic NLP techniques.
 Week 4
Lesson 4
Week 4: Advanced NLP and Sentiment Analysis
Building on the previous week, students will delve into more advanced NLP concepts. They'll learn about bag-of-words models and TF-IDF (Term Frequency-Inverse Document Frequency). The main project this week is sentiment analysis, where students will build a model to classify text as positive, negative, or neutral. They'll also create visualizations to interpret and present their model's results, reinforcing both NLP and data visualization skills.
 Week 5
Lesson 5
Week 5: Word Representations and Embeddings
This week introduces how computers represent and understand words. Students will learn about one-hot encoding and its limitations, leading to the concept of word embeddings. They'll explore popular embedding techniques like Word2Vec and GloVe. Practical exercises will include visualizing word embeddings in 2D space, allowing students to see how these models capture semantic relationships between words.
 Week 6
Lesson 6
Week 6: Recurrent Neural Networks
Building on their knowledge of neural networks and sequential data, students will learn about Recurrent Neural Networks (RNNs) this week. They'll understand why traditional neural networks fall short for sequential data like text or time series. The course will cover basic RNN architecture, the concept of hidden states. The hands-on component includes implementing a simple RNN for text generation, giving students practical experience with these powerful models.
 Week 7
Lesson 7
Week 7: Introduction to Transformers
This week introduces students to the groundbreaking Transformer architecture. They'll learn about the key innovations in Transformers, including self-attention mechanisms and positional encodings. Students will understand why Transformers have revolutionized NLP tasks. The practical session includes visualizing attention mechanisms to help students grasp this complex but powerful concept.
 Week 8
Lesson 8
Week 8: Large Language Models
Building on the previous week, students will explore Large Language Models (LLMs) like GPT. They'll learn about the scale of these models, their training process, and their diverse capabilities. Students will get hands-on experience using pre-trained models for text generation. The week concludes with a mini-project where students create a simple chatbot using a pre-trained model, providing practical experience with cutting-edge AI technology.
 Week 9
Lesson 9
Week 9: AI Applications and Ethics
This week broadens students' understanding of AI applications, particularly focusing on the capabilities of LLMs in tasks like translation, summarization, and question-answering. Students will be introduced to prompt engineering, learning how to effectively communicate with AI models. Through case studies and group discussions, students will explore the societal impacts of AI, issues of bias and fairness, privacy concerns, and the importance of responsible AI development.
 Week 10
Lesson 10
Week 10: Final Projects and Presentations
The final week is dedicated to completing and presenting group projects. Students will work in teams to design an AI-powered application that incorporates data analysis and visualization. They'll create mock-ups of their application, explain the AI/ML techniques their app would use, and discuss potential ethical considerations. The week includes project presentation sessions, where each team will showcase their work to the class.