Schedule

Worksheet

• Worksheet 1

In-Class Activities

1. Perceptions of AI
   Individual reflection and small-group discussion.

2. Reading and Reflection
   Read this article and respond to guided questions.

3. Class Discussion: Responsibility
   Whole-class discussion on responsibility and accountability in AI.

Submission

• Upload a PDF of the completed worksheet to Gradescope under In-Class – Week 1 Tuesday

Worksheet

• Worksheet 2

In-Class Activities

1. What Makes Something Intelligent?

   Group Activity: Small-group discussion using a shared board.

2. Big Ideas in AI

   Short discussion on perception, representation, learning, interaction, and societal impact.

   Worksheet activity applying the Big Ideas to:

   • Face detection on phones

   • A biological intelligence example

   • Another AI system

   Resources: Big Ideas Poster

3. Areas where AI is Used

   Short discussion on major AI application areas (e.g., vision, language, recommendation systems, robotics).

   Worksheet Activity: Connecting AI areas to CSE courses.

Submission

• Upload a PDF of the completed worksheet to Gradescope under In-Class – Week 1 Thursday

Worksheet

• Worksheet 3

In-Class Activities

1. The Imitation Game (Turing, Sections 1–5)
   Individual reflection followed by small-group discussion on what the Imitation Game captures about intelligence and what it leaves unresolved.

2. Learning Machines (Turing, Section 7)

   Read and discuss different parts of Turing’s argument about learning machines, then explain your section to your group.

3. Engineered vs. Learned Intelligence
   Work in small groups to classify AI systems as engineered or learned and justify your reasoning.

4. What Is Required for Learned Intelligence Reflect individually on what a system needs in order to learn, then contribute your ideas to a shared class list.

Submission

• Upload a PDF of the completed worksheet to Gradescope under In-Class – Week 2 Tuesday

Worksheet

• Worksheet 4

In-Class Activities

1. Engineered vs. Learned Intelligence
   Work in small groups to classify AI systems as engineered or learned and justify your reasoning.

2. What Is Required for Learned Intelligence Reflect individually on what a system needs in order to learn, then contribute your ideas to a shared class list.

3. Machine Learning Pipeline Build a high-level machine learning pipeline, discuss the role of each stage, and reason about why learning systems are iterative.

4. Learning Paradigms Identify whether example systems use supervised, unsupervised, or reinforcement learning and explain why.

5. Designing a Learning System Individually propose a learning system by defining the problem, the type of data or experience needed, the learning paradigm, and what success would look like. (PLEASE SUBMIT THIS AS HW2)

Submission

• Upload a PDF of the completed worksheet to Gradescope under In-Class – Week 2 Thursday

Log in to DataHub using your UCSD account. Then click on the worksheet link below - it should directly open the worksheet in DataHub.

Worksheet

• Open on DataHub directly: Worksheet 5

• If the DataHub link does not work, download the notebook from GitHub and upload it to DataHub.

Quick Poll

Please complete this short poll at the start of class: Poll Link

In today’s class we will:

• Continue reviewing Worksheet 5 and finish the final exercise.

• Review the material we have covered so far.

• Address any specific topics you would like to revisit and work through a few sample questions together.

Worksheet

• Open on DataHub directly: Worksheet 5

• If the DataHub link does not work, download the notebook from GitHub and upload it to DataHub.

Submission

• Upload a PDF of the completed worksheet to Gradescope under In-Class – Week 3 Thursday

• To receive credit, your submission must include an attempted solution for Exercise: Brute-Force Search for Celsius to Fahrenheit.

Test 1 Prep:

• Please review the Test 1 preparation notes and let us know if you have any questions or clarifications.

• We will work thruogh the Practice Set 1 on prairielearn.