Courses
- ECE 150 - Making Better Decisions by Humans and AI (4 credits at UMass Amherst)
- ECE 214 - Probability and Statistics (4 credits at UMass Amherst)
- ECE 579 - Math Tools for Data Science & Machine Learning (3 credits at UMass Amherst)
- ECE 601: Machine Learning for Engineers (3 credits at UMass Amherst)
- ECE 603 - Probability & Random Process (3 credits at UMass Amherst)
- Applying Risk & Chance to Life & Business
- Brief Introduction to Machine Learning (No Coding)
Gen Ed Course (SB, DU, 4 credits)
E&C-ENG 150: Making Better Decisions by Humans and AI
Online Course
Jul 7, 2025 - Aug 15, 2025 Enroll NowInstructor: Hossein Pishro-Nik
This course covers decision making under uncertainty, focusing on topics such as evolutionary psychology, human biases, probabilistic thinking, risk taking, artificial intelligence, AI biases and algorithmic oppression. The skills learned in this class can aide students in decision making at both personal and societal levels. They can help students recognize cognitive and algorithmic biases and comprehend the social implications of these biases. Examples from everyday decisions, business/finance, economics/policy making, sports, and AI decision making are discussed. There are no prerequisites for this class and students from all majors may enroll. The course satisfies SB and DU designations and is worth 4 credits.
ECE 214: Probability & Statistics (4 credits)
Online CourseJul 7, 2025 - Aug 15, 2025 Enroll Now
Instructor: Hossein Pishro-Nik
Syllabus
This course is offered online and covers most of the material in the first 9 chapters of the textbook. It includes detailed video lectures, online homework assignments, and exams. It is designed to satisfy the probability and statistics requirement of most engineering programs. Face-to-face office hours at UMass Amherst is also available for local students.
Note about pre-requisite: The course is accessible to students who have successfully finished a course in Calculus. If you would like to register for the course, but are not able to do so due to the official pre-requisite, you can request a permission to register by sending an email to the instructor.
Topics covered
- (Chapters 1 and 2): basic concepts such as random experiments, probability axioms, conditional probability, law of total probability, Bayes' rule, and counting methods
- Chapters 3 through 6): single and multiple random variables (discrete, continuous, and mixed), functions of random variables, joint distributions, sum of random variables, moment-generating functions, random vectors, and inequalities
- Chapter 7): law of large numbers and the central limit theorem
- Chapters 8 and 9): Bayesian and classical statistical inference: Point and interval estimation, hypothesis testing, and linear regression.
ECE 579: Math Tools for Data Science & Machine Learning (3 credits)
Online Course - Same course offered twice; choose one session:
May 19, 2025 - Jul 1, 2025 Enroll Now
Jul 7, 2025 - Aug 15, 2025 Enroll Now
Instructor: Hossein Pishro-Nik
Syllabus
Success in the field of data science heavily depends on students’ knowledge of many mathematical tools that are not always covered in standard mathematical curriculum. Often, these topics are not covered appropriately in data science courses either due to the time limits. This course aims at filling this void: it covers mathematical tools needed for courses in data science such as machine learning, data mining, neural networks, etc. It motivates the topics by real-word applications and discusses how they can be used in data analytics algorithms.
Topics covered
- Linear Algebra
- Vectors and Matrices and basic operations
- Vector spaces
- Eigenvalues and eigenvectors
- Singular value decomposition (SVD) and application to dimensionality reduction
- Probability and Statistics
- Basic probability, conditional probability, Bayes’ rule
- Random variables and random vectors
- Probability bounds
- Markov chains
- Application to web search algorithms: Link analysis and Page Rank
- Foundations of Statistical Learning
- Basics of statistical learning: models, regression, curse of dimensionality, overfitting, etc.
- Optimization and convexity
- Gradient descent
- Newton’s method
- Classification
- Linear discriminant analysis
- Logistic Regression
- Support vector machines (SVM)
- Additional methods
- Similarity and distances
- Nearest neighbor methods
- Decision tress and application of entropy
- Clustering, Graph Analysis and Algorithms
- Clustering algorithms
- Social network graphs
- Community detection
- Additional topics
- Knowledge driven feature design
- Basics of neural networks
ECE 601 - Machine Learning for Engineers (3 credits)
Online Course May 19, 2025 - Jul 1, 2025 Enroll Now
Instructor: Hossein Pishro-Nik
This course will cover the mathematical underpinnings, algorithms, programming, and practices that enable a computer to learn. Starting with the foundational elements of machine learning, this course covers training, evaluation, loss functions, (stochastic) gradient descent, linear regression, and logistic regression. It then progresses to common deep learning models, including feedforward networks, convolutional networks, graph neural networks, attention models, and transformers. The course also addresses practical aspects of deep learning such as parameter tuning, large-scale model training, regularization techniques, and prevention of overfitting. Additionally, it includes a brief and basic introduction to more advanced techniques like deep reinforcement learning and generative models. The prerequisites for this course include introductory courses in linear algebra, calculus, and probability. Knowledge of Python programming is necessary to complete computer assignments and projects.
Course Goals and Objectives:
This course is designed to deepen students' understanding and enhance their skills in machine learning coding, with an emphasis on solving real-world applications in areas such as health, control, business prediction, and pattern recognition problems. The objectives are structured as follows:
- Machine Learning Fundamentals: Students acquire fundamental knowledge of learning theory. They learn how to design and evaluate both supervised and unsupervised machine learning algorithms, exploring various widely used algorithms and critically analyzing their benefits and drawbacks.
- Practical Application: A key objective is to equip students with the tools to apply machine learning algorithms to real data and to evaluate their performance effectively.
- Critical Analysis and Implementation: The course will enhance students' ability to analyze, implement, and evaluate research in the field of machine learning, thereby deepening their understanding of the challenges related to the application of ML in various domains.
ECE 603 - Probability & Random Process (3 credits)
Online Course May 19, 2025 - Jul 1, 2025 Enroll Now
Instructor: Hossein Pishro-Nik
This course is offered online and is a graduate-level version of ECE 214. It covers chapters 1 to 7 and 10 & 11 of the textbook.
Note about pre-requisite: If you would like to register for the course, but are not able to do so due to the official pre-requisite, you can request a permission to register by sending an email to the instructor.
Topics covered
- Basic concepts such as random experiments, probability axioms, conditional probability, law of total probability, Bayes' rule, and counting methods;
- Single and multiple random variables (discrete, continuous, and mixed), as well as moment-generating functions, characteristics functions, random vectors, and inequalities;
- Limit theorems and convergence;
- Introduction to random processes, processing of random signals;
- Poisson processes, discrete-time Markov chains, continuous-time Markov chains, and Brownian motion;
Applying Risk & Chance to Life & Business
Decision Making Under Uncertainty
Learn the basic concepts and tools to help you make better decisions under uncertainty, take calculated risks, and reduce the stress and regrets that often come with decision making. This video is the 1st of a total of 40 short videos. Click here to watch the rest of the videos.
Use probabilistic thinking to increase your chance of success and manage risks
- The laws of probability that govern our life
- Biases and fallacies that often distort our decision making
- Making decisions under uncertainty
- Taking calculated risks in life and business
- Reducing stress in decision making
- Reducing regrets about past decisions
Improve your real life decision making and reduce stress and regrets
Every day, we have to make decisions but we are often unsure of what to do because of the risks and uncertainties involved. Many of us often regret decisions both big and small, but there are actually many ways we can improve our decision-making and risk management, and reduce the stress and regrets about decisions.
Content and Overview
In this course, our goal is to better understand randomness and uncertainty and learn tools to help us make more educated risks. We'll talk about the different biases we all experience in our intuitive thinking, and then learn how to re-train our brains to approach everyday problems differently. Using probability theory and a bit of math, we'll discuss how to make decisions rationally and efficiently. But don't worry—no math background other than being able to add, subtract, divide, and multiply is required! We'll learn how to make better financial decisions, take smarter risks, and improve nearly every aspect of our lives. Each video is short and concise but filled with interesting and helpful material. Each one is animated, to ensure we grasp the concepts completely, and they all contain engaging, relatable real-life examples. Using these tools, anyone can learn to improve their decision-making, which leads to ultimately minimizing the number of regrets they have. If you'd like to live a more worry-free life with fewer regrets, this course is for you.
Acknowledgement
Special thanks to Linnea Duley for her great help in preparing the content as well as excellent job in creating the animations.
Brief Introduction to Machine Learning (No Coding)
In a series of few short videos, we will go over a general, non-technical introduction to Machine Learning (ML). We will define and explain a few fundamental concepts in ML, including overfitting, cross-validation, VC-dimension, regularization and others. This module is designed to help a general audience, including newcomers. My hope is that this lesson aids in understanding what applications are best suited for ML, provides intuition behind ML algorithms and conveys the importance of ML in today’s world. This video is the 1st of a total of 7 short videos. Click here to watch the rest of the videos.