Applied Artifical Intelligence (AAI)

Welcome: introduction to the Applied Artificial Intelligence Bootcamp (1hr 30m).

PDF material can be downloaded from the releases page.

The program consists of two tracks each aimed at a specific career path:

1. Data Scientist
2. AI Engineer

Pre-requisites

Both tracks presume the following about the learner to get started:

• English B2 (Upper-Intermediate) level: IELTS 6.5 or TOEFL 80.
• Algorithmic thinking and problem-solving skills.
• Strong foundation in programming.
• Working laptop with internet access.

1. Terminal

Command and conquer your machine. Fear not the black box. Protect yourself from malicious code.

Time Estimate: 1 day x 3 hours.

Track 1: Data Scientist

Time Estimate: 4-5 weeks at 30 hrs/week.

1. Data Wrangling

Fundamentals of data wrangling and analysis in Python via pandas, matplotlib and seaborn.

• M1. Filtering, Sorting, and Aggregation
• M2. Data Wrangling
• M3. Data Vizualization
• M4. Timeseries Analysis

Time Estimate: 5 days x 6 hours.

2. Data Science

Calculate, analyze, visualize, and extract insights from data. Formulate hypotheses and draw conclusions.

• M1. Introductions
• M2. Univariate Analysis
• M3. Bivariate Analysis
• M4. Inferential Statistics

Time Estimate: 5 days x 6 hours.

3. Applied Machine Learning

Build reliable predictive modeling pipelines, debug its issues, evaluate and compare alternatives.

• M1. Supervised ML: Regression and Classification
• M2. Estimating and Improving Model Generalization Performance
• M3. Pipeline: Building Reliable Predictive Models
• M4. Decision Trees and Ensembles
• M5. AutoML

Time Estimate: 10 days x 6 hours.

Track 2: AI Engineer

Time Estimate: 4-5 weeks at 30 hrs/week.

1. Agentic Engineering

Work effectively and efficiently with AI in software engineering projects.

• M1. From Vibe Coding to Agentic Engineering
• M2. Skills for Engineers
• M3. Agent Modes

Time Estimate: 5 days x 6 hours.

2. Building Agentic AI Software

Develop, debug, evaluate, deploy, and monitor LLM-driven AI Agents to automate tasks involving unstructured data.

• M1. Signatures and Modules
• M2. Agents with Tools
• M3. Coding Agents
• M4. Optimization
• M5. Retrieval Augmented Generation (RAG)

Time Estimate: 10 days x 6 hours.

3. Applied Deep Learning

Select, use, compose, fine-tune, and deploy open-weight deep learning models on various unstructured data tasks.

• M1. HuggingFace and Large Language Models
• M2. Realtime Computer Vision Models

Time Estimate: 5 days x 6 hours.

AI Policy

Good use of AI means it augments, rather than replaces, thinking — used for feedback, hints, explanations, practice, or extra resources, while you still do the core reasoning, writing, and problem-solving.

Forbidden use: treating course material as "work" and AI as an assistant to get it done "faster" or "easier" or "better". Don't mix productivity (output) with learning (you).

See the research and findings that made up our AI Policy.

Assigned Exercises

• Due Thursday 11:59 PM (end of same week).
• Work must have been pushed to GitHub.
• Marked as done (in Google Classroom) before then.
• Commit history MUST follow the proof-of-work system.

---

Contribution (Course Development)

• Local dev unchanged: just compile / just watch still produce ignored local PDFs.
• To publish: git tag v1.0 && git push origin v1.0 -> workflow builds and attaches PDFs to the v1.0 release, which becomes latest.