Minor in AI

 1501220

 Advanced Python Programming

Credit hour 3

Prerequisite:

1501116 Programming I OR 1501113 Programming for Engineers

Description: This course covers advanced features of the Python programming language, with an emphasis on programming practice. It aims to help learners understand the semantics of Python and the process of structuring data using collections such as lists, dictionaries, tuples, strings, and sets. Learners will be guided to develop important skills, including working with classes, data structures, and sorting algorithms, as well as implementing threads and exception handling. The course involves substantial Python programming practice through projects and assignments.

1501315

Practical Data Science

Credit hour 3

Prerequisite:

1501220 Advanced Python Programming

Description: Data science is an interdisciplinary field that uses statistics, mathematics, programming, and domain knowledge to extract insights from data. This course introduces students to key concepts such as data collection and Wrangling, data cleaning and normalization, data visualization and exploration. Students will learn to use Python tool and will gain hands-on experience in working with real-world datasets to solve complex problems and make data-driven decisions.

1501330

Intro to AI

Credit hour 3

Prerequisite:

1501220 Advanced Python Programming

OR 1501214 Prog. With Data Structures OR 1501215 Data Structures

Description: This course will provide an introduction to the fundamental concepts and techniques in the field of artificial intelligence. Topics covered in the course include basic search, heuristic search, game search, constraint satisfaction, knowledge representation, and machine learning algorithms, Perception, Robotics, and Natural Language Processing.

1501331

Intro. Machine Learning

Credit hour 3

Prerequisite:

1501220 Advanced Python Programming

Description: This course offers a foundational and practical exploration of machine learning, covering essential models and techniques for building and evaluating predictive systems. Students begin with core concepts, including feature types, model selection, and the bias-variance tradeoff. Key models explored include classification (k-nearest neighbors, logistic regression, and Gaussian Naive Bayes), regression (linear and elastic net), support vector machines, decision trees, and ensemble methods. Advanced topics introduce neural networks, deep learning architectures, and clustering techniques for unsupervised learning. The course emphasizes hands-on applications, preparing students to effectively design, assess, and improve machine learning models for practical applications.

1501433

Intro to Computer Vision and Image Proc.

Credit hour 3

Prerequisite:

1501220 Advanced Python Programming

OR 1501214 Prog. With Data Structures OR 1501215 Data Structures

Description: Introduction to the basic concepts in computer vision and image processing: An introduction to low-level image analysis methods, including image formation, edge detection, feature detection, line fitting, and image segmentation. Camera models, Image transformations (e.g., warping, morphing, and mosaics) for image synthesis. Background subtraction and tracking, Motion and video analysis. Applications such as optical character recognition, action recognition or face recognition may also be introduced.

1501446

Intro. to Deep Learning

Credit hour 3

Prerequisite:

1501331 Intro. to Machine Learning

Description: This course provides a comprehensive introduction to the foundational concepts and cutting-edge techniques in deep learning. Key topics include gradient descent, neural networks, and backpropagation. Topics include advanced architecture like CNNs, RNNs, LSTMs, Autoencoders, and GANs, as well as modern approaches like Diffusion Models, Graph Neural Networks, and Transformers. Applications include Reinforcement Learning, Recommender Systems, and NLP with tools like NLTK and SpaCy, equipping students with essential skills in AI and deep learning.

1501466

Data Analytics & Visualization

Credit hour 3

Prerequisite:

1501315 Practical Data Science

Description: This course exposes students to data analytics and visualization processes and provides hands-on instructions and coding exercises. Particularly, the course discusses the basics of Python programming, fundamental structures, data collection structures, file I/O processing, data exploration techniques, data gathering and cleaning techniques, statistical data analysis methods, and data visualization tools, such as direct plots, Seaborn plots, and Matplotlib plots.

1501493

Topics in AI

Credit hour 3

Prerequisite:

1501330 Intro. To AI

Description: This course introduces AI and search techniques, covering fundamental problem-solving methods. Students will study uninformed and heuristic searches, local search methods, and constraint satisfaction solutions. The course progresses through evolutionary algorithms, population-based metaheuristics, and hybrid approaches. Advanced topics include hyperparameter optimization and real-world applications in healthcare, finance, language processing, and computer vision, equipping students with practical AI problem-solving skills.