Paolo Prandoni

COM-303

Python3

Interactive textbook

English

This notebook explores the advantages of digital transmission over analog transmission, focusing on signal attenuation and noise in long-distance communication. Students will understand how digital signals can be regenerated to maintain quality over long distances, using concepts like quantization and signal-to-noise ratio (SNR).

Paolo Prandoni

COM-303

Python3

Interactive textbook

English

This notebook introduces the Karplus-Strong algorithm, a method for generating synthetic sounds with arbitrary pitch. Students will learn about discrete-time signal processing concepts, implement variations of the algorithm, and explore its application in creating musical notes and chords. By the end, students will understand how to manipulate sound properties like pitch and decay using digital signal processing techniques.

Paolo Prandoni

COM-303

Python3

Interactive textbook

English

This notebook demonstrates how to simulate the effect of a standard telephone channel using a Finite Impulse Response (FIR) filter. Students will learn to design a passband filter using the Remez algorithm and apply it to an audio signal to observe the frequency response. By the end, students will understand how to model and analyze the frequency characteristics of a telephone channel.

Paolo Prandoni

COM-303

Python3

Interactive textbook

English

This notebook explores the effects of a telephone channel on data transmission schemes, focusing on naive transmission and Frequency-Shift Keying (FSK). Students will understand how to fit signals to a channel and analyze signal distortion using frequency domain analysis.

Johanni Michael Brea

BIO-322

Julia

Exercise worksheets

English

This notebook provides an introduction to multilayer perceptrons (MLPs), a fundamental concept in neural networks. Students will learn about the architecture of MLPs, how they are trained using backpropagation, and their application in classification tasks. By the end, students will understand the role of activation functions and the importance of hyperparameter tuning.

Johanni Michael Brea

BIO-322

Julia

Exercise worksheets

English

This notebook introduces the fundamental concepts of reinforcement learning, focusing on the learning goals of understanding policy optimization and value functions. Students will explore methods such as Q-learning and policy gradients, and by the end, they will be able to implement basic reinforcement learning algorithms. Key datasets and environments like OpenAI Gym are utilized for practical understanding.

Johanni Michael Brea

BIO-322

Julia

Exercise worksheets

English

This notebook provides an introduction to neural networks, focusing on their structure, function, and practical implementation. Students will learn about key concepts such as layers, activation functions, and backpropagation. By the end, students will understand how to build and train a simple neural network using Python.

Johanni Michael Brea

BIO-322

Julia

Demonstrations in class

English

This notebook explores the concept of flexibility in computational models, focusing on how different algorithms can adapt to changing inputs. Students will learn about key flexibility metrics and apply them to sample datasets. By the end, students will understand how to evaluate and improve model flexibility.

Johanni Michael Brea

BIO-322

Julia

Exercise worksheets

English

This notebook introduces students to data transformation techniques using Python. By the end, students will understand how to apply various transformations to datasets, including normalization and scaling. Key algorithms such as Principal Component Analysis and feature scaling methods are covered.

Cécile Hardebolle

CS-290

Python3

Exercise worksheets

English

Introductory exercise notebook for Responsible Software covering Python basics, data analysis and visualization, and a basic ethical dilemma scenario.