Artificial Intelligence (CECS 451)

Undergraduate course, CSU Long Beach, 2024

Course Outline

This course delves into the foundational concepts and algorithms of modern artificial intelligence (AI); it offers a hands-on approach where students learn through projects, covering topics such as search algorithms, optimization, supervised learning, and probabilistic inference, all within the context of AI and machine learning. The course involves practical Python programming, where students use AI libraries as well as their own implementation of AI algorithms to solve different puzzles.

Artificial Intellegence(CECS 451)
InstructorDr. Arash Sarshar (email)
TermSpring 2024
Class Day & TimeFr 12:00PM - 2:45PM
Class locationECS 403
Office HoursTBD (Check Canvas)

Learning Outcomes

The primary objectives of this course is for the students to be able to:

  • Understand and utilize AI models and algorithms.
  • Critically analyze the present trends and challenges in AI research.
  • Construct AI-based solutions to a variety of practical problems.
  • Demonstrate understanding of the repercussions and ethical considerations of AI technologies.


Probability, Statistics, and Linear Algebra Review

  • Basic concepts of probability: random variables, probability distributions, expected value.
  • Conditional, joint, and marginal distributions.
  • Statistical measures: mean, median, mode, standard deviation, and correlation.
  • Vector spaces, Norms, and Matrix calculation.

Search Algorithms

  • Search Problems.
  • Graph search algorithms: Depth-First, Breadth-First, and Greedy Search.
  • $A^*$ Search, Minimax, and $\alpha-\beta$ Pruning.

Logical Inference

  • Propositional Logic.
  • Inference on a knowledge base.
  • Model Checking and other methods of resolution.
  • First Order Logic.

Inference Under Uncertainty

  • Bayesian Networks.
  • Sampling methods: Direct and Weighted sampling.

Finding Optimal Solutions

  • Optimization methods: Hill Climbing, Simulated Annealing, and other stochastic methods.
  • Linear Programming.
  • Constraint Satisfaction problems.

Machine learning for AI

  • Supervised Learning: Nearest-Neighbor Classification, Linear models, Support Vector Machines.
  • Unsupervised Learning: k-means Clustering.
  • Reinforcement Learning: Markov Decision Processes, Q-Learning.

Deep Learning for AI

  • Neural Networks: Layers, activation, back-propagation, and training of neural networks.

Ethics of AI

  • Understanding AI ethics: bias in machine learning.
  • Fairness, accountability, and transparency in AI systems.
  • Privacy and security considerations in AI applications.
  • Real-world case studies on AI ethics and responsible AI.

Learning Resources

Readings will be assigned from open educational resources based on the topic. If you are inteersted in building foundational knowledge in machine learning and AI, here are a number of excellent books you can read:

Special Accommodations

We are committed to providing equal opportunities for all students in our courses. If you require any special accommodations due to a documented disability, medical condition, or other circumstance that may affect your ability to fully participate in the course, please notify me as early as possible. Also, please contact the Bob Murphy access center (BMAC) for more information. BMAC personnel will work with students to identify a reasonable accommodation in partnership with appropriate academic offices and medical providers. Only approved BMAC petitions will be accommodated.

Any student who is facing academic or personal challenges due to difficulty in affording groceries/food and/or lacking a safe and stable living environment is urged to contact the CSULB Student Emergency Intervention & Wellness Program. Additional resources are available via Basic Needs Program.For mental health assistance please check out CSULB Counseling and Psychological Services (CAPS).

Academic Integrity

Before attending the class, please make sure that you have invested some time to read about CSULB academic integrity policy here. Of particular importance, are the definitions of Plagiarism and Cheating, and the steps the university will take to adjudicate academic integrity cases.

Work that you submit is assumed to be original unless your source material is documented appropriately, using proper citation. This includes using AI assistance in writing, coding and solving problems. At a minimum, any student caught violating Academic Integrity Policy will receive no credit for the work concerned and one grade lower letter grade.


Please be aware that this course outline may change without prior notice. There may be instances where adjustments are needed for the course content, schedule, or grading policy. As a result, it is important that students check the official course syllabus on canvas regularly for the latest updates.