AI-2022spring

Book chapters and video lectures

Chapter 0: Initialization

Readings:

1. Reading: Distance killing
2. Reading: AlphaFold is the most important achievement in AI ever

Video lectures:

1. Lecture: AlphaGo
2. Lecture: Algorithmic bias and fairness
3. Lecture: Military robots and future of war
4. Lecture: The real reason to be afraid of AI

Chapter 19: Learning from Examples

Book sections (readings):

• 19.1 Forms of Learning
• 19.2 Supervised Learning
• 19.3 Learning Decision Trees
• 19.4 Model Selection and Optimization
• 19.5 The Theory of Learning
• 19.6 Linear Regression and Classification
• 19.7 Nonparametric Models
• 19.8 Ensemble Learning
• 19.9 Developing Machine Learning Systems

Video lectures:

1. Forms of learning
2. Univariate linear regression
3. Activity: Univariate linear regression using Tensorflow Keras
4. Linear classifiers and logistic regression
5. Activity: Logistic regression using tensorflow keras
6. Artificial neural networks
7. Activity: Binary classification using tensorflow keras

Chapter 1: Introduction

Book sections (readings):

• 1.1 What Is AI?
• 1.2 The Foundations of Artificial Intelligence
• 1.3 The History of Artificial Intelligence
• 1.4 The State of the Art
• 1.5 Risks and Benefits of AI

Video lectures:

1. What is AI?
2. Acting humanly and thinking humanly
3. Thinking rationally and acting rationally
4. Foundations of AI
5. History of AI

Chapter 2: Intelligent Agents

Book sections (readings):

• 2.1 Agents and Environments
• 2.2 Good Behavior: The Concept of Rationality
• 2.3 The Nature of Environments
• 2.4 The Structure of Agents

Video lectures:

1. Agent function vs agent program (first 20 minutes)
2. Rationality (last 15 minutes)

Chapter 3: Solving Problems by Searching

Book sections (readings):

• 3.1 Problem-Solving Agents
• 3.2 Example Problems
• 3.3 Search Algorithms
• 3.4 Uninformed Search Strategies
• 3.5 Informed (Heuristic) Search Strategies
• 3.6 Heuristic Functions

Video lectures:

1. Sample search problems
2. Searching algorithm concepts
3. Uninformed search algorithms
4. Informed search algorithms

Chapter 5: Adversarial Search

Book sections (readings):

• 5.1 Game Theory
• 5.2 Optimal Decisions in Games
• 5.3 Heuristic Alpha–Beta Tree Search
• 5.4 Monte Carlo Tree Search

Video lectures:

1. Introduction to adversarial search
2. Minimax algorithm
3. Alpha-beta pruning algorithm
4. Online solver

Chapter 6: Constraint Satisfaction Problems

Book sections (readings):

• 6.1 Defining Constraint Satisfaction Problems

Chapter 7: Logical Agents

Book sections (readings):

• 7.1 Knowledge-Based Agents
• 7.2 The Wumpus World
• 7.3 Logic
• 7.4 Propositional Logic: A Very Simple Logic

Video lectures:

1. Knowledge base agents (logical) and a bit of entailment
2. Entailment

Chapter 12: Quantifying Uncertainty

Book sections (readings):

• 12.1 Acting under Uncertainty
• 12.2 Basic Probability Notation
• 12.3 Inference Using Full Joint Distributions
• 12.4 Independence
• 12.5 Bayes’ Rule and Its Use
• 12.6 Naive Bayes Models

Chapter 13: Probabilistic Reasoning

Book sections (readings):

• 13.1 Representing Knowledge in an Uncertain Domain
• 13.2 The Semantics of Bayesian Networks
• 13.3 Exact Inference in Bayesian Networks
• 13.4 Approximate Inference for Bayesian Networks

Chapter 21: Deep Learning

Book sections (readings):

• 21.1 Simple Feedforward Networks
• 21.2 Computation Graphs for Deep Learning
• 21.3 Convolutional Networks
• 21.4 Learning Algorithms
• 21.5 Generalization
• 21.6 Recurrent Neural Networks
• 21.7 Unsupervised Learning and Transfer Learning
• 21.8 Applications

Chapter 23: Natural Language Processing

Book sections (readings):

• 23.1 Language Models

Video lectures:

1. N-gram character models
2. N-grams for language identification
3. How to detect spams using N-grams?

Chapter 24 Deep Learning for Natural Language Processing

Book sections (readings):

• 24.1 Word Embeddings

Chapter 25: Computer Vision

Book sections (readings):

• 25.1 Introduction
• 25.2 Image Formation
• 25.3 Simple Image Features
• 25.4 Classifying Images

Video lectures:

1. Edge detection using convolution and smoothing
2. Smoothing an image using convolution
3. Optical flow
4. Image segmentation
5. AI art sells

Chapter 26: Robotics

Book sections (readings):

• 26.1 Robots
• 26.2 Robot Hardware
• 26.3 What kind of problem is robotics solving?

Video lectures:

1. Types of sensors
2. How does GPS work?
3. Range finders
4. Skip: What is degree of freedom in a robot?
5. Skip: Path planning in moving robots
6. ‘Atlas’ by Boston Dynamics
7. ‘Spot’ by Boston Dynamics
8. ‘Autonomous pizza robot’

Chapter 27: Philosophy, Ethics, and Safety of AI

Book sections (readings):

• 27.1 The Limits of AI
• 27.2 Can Machines Really Think?
• 27.3 The Ethics of AI

Video lectures:

1. Lecture: How AI could reinforce biases in the criminal justice system
2. Lecture: From Model-centric to Data-centric AI
3. Reading: Beware of explanations from AI in health care
4. Lecture: Can machines really think?
5. Lecture: Will people lose their sense of being unique?

Readings:

1. Giving GPT-3 a Turing Test

Chapter 28: The Future of AI

Book sections (readings):

• 28.1 AI Components
• 28.2 AI Architectures