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Schedule

 WeekLecture Topic Homework (datacamp.org)
Module I: Understanding Artificial Intelligence

1

8/31

Course Introduction
  • The instructor, the outline, classroom conduct, academic integrity, attendance, and grading policy.
What is Artificial Intelligence (AI)?
  • Different Views and Approaches to AI
  • AI vs ML vs DL
  • AI in Industry
  • AI in Enterprise
Course: Understanding Artificial Intelligence
  • Chapter 1 – What is Artificial Intelligence (AI)?
  • Chapter 2- Tasks AI can solve

2

9/7

The Power of AI and Its Human Side
  • Ingredients to AI-driven organizations
  • Best practices for AI democratization
Course: Understanding Artificial Intelligence
  • Chapter 3 – Harnessing AI in Organizations
  • Chapter 4- The human side of AI

3

9/14

ChatGPT and Generative AI

  • What can ChatGPT do?
  • Enabling people to use GenAI
  • Legal and ethical considerations
Course: Introduction to ChatGPT
  • Chapter 1 – Interacting with ChatGPT
  • Chapter 2- Adopting ChatGPT
Module II: Intro to Machine Learning

4

9/21

EXAM I (Module I)

Introduction to Supervised Learning

  • Regression

  • Classification
Course: Understanding Machine Learning
  • Chapter 1 – What is Machine Learning?
  • Chapter 2- Machine Learning Models

5

9/28

Introduction to Unsupervised Learning

  • Clustering with K-Means
Course: Understanding Machine Learning
  • Chapter 3- Deep Learning

6

10/5

Introduction to Deep Learning
  • Neural Networks
Course: Understanding Machine Learning
  • Chapter 3- Deep Learning
Module III: Generative AI

7

10/12

EXAM II (Module II)

Natural Language Processing

Course: Large Language Models (LLMs) Concepts
  • Chapter 1- Introduction to Large Language Models (LLM)
  • Chapter 2 – Building Blocks of LLMs

8

10/19

Transformers

Course: Large Language Models (LLMs) Concepts
  • Chapter 3- Training Methodology and Techniques
  • Chapter 4 – Concerns and Considerations

9

10/26

Neural Networks: Representation

  • Non-linear Hypotheses
  • Neurons and the Brain
  • Model Representation
  • Example and Intuition
  • Multiclass Classification
Course: Generative AI Concepts
  • Chapter 1- Introduction to Generative AI
  • Chapter 2- 
    Developing Generative AI Models

10

11/2

Neural Networks: Learning
  • Learning Cost Function
  • Backpropagation Algorithm
  • Backpropagation Intuition
Course: Generative AI Concepts
  • Chapter 1- Introduction to Generative AI
  • Chapter 2- 
    Developing Generative AI Models

11

11/9

Deep Reinforcement Learning
  • Q-Learning
  • Deep Reinforcement Learning
  • Policies and Learning Algorithms
Course: Generative AI Concepts
  • Chapter 3- Using AI Models and Generated Content Responsibly
  • Chapter 4- 
    Getting Ready for the Age of Generative AI

12

11/16

Linear Classifiers in Python
  • Applying Logistic Regression and SVM
  • Loss function
Course: AI Ethics
  • Chapter 1- Approaching AI Ethics

13

11/23**

**No class. Happy Thanks Given!!
PART IV: Disconnected Study

14

11/30

EXAM IV (Module III) 

Final Project

TBD

15

12/7

Final ProjectTBD

16

12/14

Final Exam and Final Project Presentation
Fall 2023 Class Schedule