Applied Machine Learning (CECS 457)

Undergraduate course, CSU Long Beach, 2023

Course Outline

Fundamentals of machine learning with an emphasis on the process of applying machine learning techniques to various real-world computing applications, including computer vision, natural language processing, data analytics and forecasting.

Applied Machine LearningCECS 457
InstructorDr. Arash Sarshar (email)
TermFall 2023
Class Day & TimeMW 3:30 ⎯ 4:45PM
Class locationFCS-126
PrerequisitesCECS 456
Office HoursTBD (Please Check Canvas)

Learning Outcomes

  • Acquire a good understanding of the theory, algorithms, and applications of machine learning (ML) and develop practical skills in using ML in real-world applications.
  • Apply machine learning and software development fundamentals to produce ML solutions for various problems and use the results to make data-driven decisions.
  • Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles.


Module: Probability, Statistics, and Linear Algebra Review (2 weeks)

  • Basic concepts of probability: random variables, probability distributions, expected value, variance, and covariance.
  • Vector spaces, Norms, and Matrix calculation
  • Linear systems of equations, and Eigenvalue problems.
  • Statistical measures: mean, median, mode, standard deviation, and correlation.
  • Regression analysis and linear models.
  • Practical project: Analyzing a dataset using statistical techniques.

Module: Deep Learning Review (2 week)

  • Introduction to deep neural networks and their architecture.
  • Back-propagation and automatic differentiation algorithms.
  • Optimization and regularization techniques.
  • Practical project: Building a deep learning model for a specific task.

Module: Machine Vision (2 weeks)

  • Image representation and manipulation.
  • Introduction to convolutional neural networks (CNNs).
  • Transfer learning for image classification tasks.
  • Object detection and image segmentation.
  • Practical project: Building an image classifier using CNNs.

Module: Time Series Analysis (2 week)

  • Introduction to time series data and its characteristics.
  • Forecasting techniques: ARIMA, exponential smoothing, and other methods.
  • Feature engineering for time series data.
  • Recurrent neural networks (RNNs) and LSTM for sequential data analysis.
  • Practical project: Predicting stock market trends using time series analysis.

Module: Natural Language Processing (2 week)

  • Introduction to NLP and its applications.
  • Sentiment analysis and and part-of-speech tagging.
  • Language modeling and text generation.
  • Transformers and attention mechanism.
  • Practical project: Sentiment analysis on social media data.

Module: Generative Models (2 weeks)

  • Introduction to generative models and their applications.
  • Variational Autoencoders (VAEs) and their applications.
  • Generative Adversarial Networks (GANs) and their training methods.
  • Normalizing flows and Diffusion models.
  • Practical project: Generating realistic images using GANs.

Module: Dimensionality Reduction and Manifold Learning (1 week)

  • Introduction to dimensionality reduction techniques.
  • Principal Component Analysis (PCA) and its applications.
  • Manifold learning algorithms: t-SNE, Isomap, and LLE.
  • Practical project: Visualizing high-dimensional data using dimensionality reduction techniques.

Module: Ethics and Responsible AI (1 week)

  • Understanding AI ethics and bias in machine learning.
  • Fairness, accountability, and transparency in AI systems.
  • Privacy and security considerations in AI applications.
  • Ethical guidelines and regulations for AI development and deployment.
  • Real-world case studies on AI ethics and responsible AI.
  • Practical project: Analyzing and addressing ethical implications in a machine learning application.

Learning Resources

Zero Cost Course Materials: This course does not have a required textbook. However, some readings will be assigned from open educational resources based on the topic. Here are a number of excellent machine learning books you can access for free:

  • The Little Book of Deep Learning by François Fleuret. Available through CC BY-NC-SA license. This book is a great primer for deep learning and it covers many of the pre-requisites for our class.
  • An Introduction to Statistical Learning with Applications in Python, by G. James, D. Witten, T. Hastie, and R. Tibshirani. Made available by the authors. Standing in the middle of theory and practice, this book provides a more statistical view of machine learning and is also a great reference for python implementations of traditional ML.
  • Pattern Recognition and Machine Learning, by C.M. Bishop. Available from Microsoft Research. This is the book if you want to learn machine leaning theory at a deeper level of understanding.

Evaluation Components

Every practical project in this course entails some form of coding in Python. The students will compile their findings into a brief report, which they will submit along with a link to their reproducible code on Github. The projects are not intended to be at the level of the industry, but rather to improve the students’ skills in using ML for a particular application. Maintaining sound software engineering practices and technical documentation is crucial for a successful career and as such, it is part of the evaluation.

Evaluation ComponentsPercent
8 mini-projects8 ✕ 10%
Code reproducibility5%
Report Quality5%

Extra credit will be available throughout the semester to make up for an assignment that did not earn you high marks. These will typically involve solving problems in novel ways or researching an interesting topic and reporting back to the class on it.

Grading Policy

Letter GradeScore
A90% or above
FBelow 60%

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.