Skip to main content
SNHU
Computer Science
3 credits

SNHU CS-370: Current and Emerging Trends in Computer Science

CS-370 explores machine learning and AI as the program's emerging-trends course: neural networks, reinforcement learning, and the ethics of intelligent systems. The signature project is the pirate intelligent agent — a treasure-hunt game where you implement deep Q-learning in a provided Jupyter notebook to train an agent to find the treasure.

Fennie is independent and not affiliated with Southern New Hampshire University. This is an unofficial study guide.

Build my CS-370 study plan

What makes it hard

The math and the ML concepts arrive fast for students who haven't touched Python since the early courses, and the provided notebooks only help if you understand what the code around your section is doing. Explaining why the agent learns — exploration versus exploitation, how rewards shape behavior — is graded as heavily as making it run.

What you'll cover

  • Neural networks and Keras basics
  • Reinforcement learning
  • Deep Q-learning
  • Exploration vs. exploitation
  • AI ethics and responsible computing
  • Emerging trends analysis

The CS-370 study guide

How to study for SNHU CS-370, step by step.

  1. 1

    Refresh your Python and notebooks in week 1

    CS-370 assumes you can read and modify Python in Jupyter, and for many students it's been several Java- and C++-heavy terms since IT-140. A short refresher prevents fighting syntax and concepts simultaneously.

  2. 2

    Trace the provided notebook before writing your part

    The pirate-agent project gives you most of the code and asks you to complete the Q-learning loop. Understanding what the surrounding cells do is the difference between implementing and guessing.

  3. 3

    Learn the concepts in plain English first

    Be able to explain exploration versus exploitation and how rewards shape the agent's behavior without jargon. The written portions grade that understanding directly.

  4. 4

    Budget real time for training runs

    Training the agent takes a while and rarely works on the first attempt. Start the project week early so a failed run is information, not a crisis.

  5. 5

    Put the term on a Fennie Daily Plan

    Upload the CS-370 module list and Fennie paces the readings, notebook work, and write-ups day by day to your deadlines, generating flashcards on the ML vocabulary from your actual course content. Free to get started.

    Start my CS-370 plan free

How Fennie helps with CS-370

Upload the CS-370 module list and Fennie's Daily Plans pace the readings and notebook work so training runs happen days before deadlines, not hours. Chat through the reinforcement-learning concepts until you can explain them plainly — that's what the write-ups grade — and drill flashcards on the ML vocabulary the discussions lean on.

FAQ

Is SNHU CS-370 hard?

It's conceptually the most ambitious course in the program — neural networks and reinforcement learning in 8 weeks — but the project scaffolding is generous. Students who engage with the concepts rather than just running the notebook do fine.

What is the CS-370 pirate agent project?

A treasure-hunt game where you complete a deep Q-learning implementation in a provided Jupyter notebook so the pirate agent learns to navigate a maze to the treasure. You write the training loop and explain how and why the agent learns.

Do I need machine learning experience before CS-370?

No — the course introduces the concepts. You do need readable Python and comfort with Jupyter notebooks, so refresh those in week 1 if your recent terms have been all Java and C++.

Pass CS-370 with a plan, not a cram

Upload your CS-370 materials and Fennie generates a Daily Plan paced to your deadline — plus chat, flashcards, and quizzes built from the actual course content.

Get started free

More SNHU courses