Machine Learning

Course Description


Machine learning (ML) techniques allow computers to adapt to data and solve new problems that are related to previously encountered problems, more efficiently. Such techniques allow machines to perform useful exploratory and predictive tasks without being explicitly programmed. ML finds its applications in speech recognition and synthesis, machine translation, object recognition, chat bots, question-answering, natural language understanding, anomaly detection, medical diagnosis and prognosis, autonomous vehicles and robots, time series forecasting, and much more. This introductory course covers the theoretical foundations and practical applications of ML and the design, implementation, and analysis of various ML algorithms. Students will learn to compare across and choose the most appropriate algorithms for various problem types and be able to design and implement their solutions. Students will be prepared for both industry and academia as well as for pursuing advanced courses. 

Course Objectives


The goal of this course is to get the students excited about Machine Learning and to enable them to:

Learning Outcomes


By the end of the course, students should be able to:


Course Outline






Reference books







Fall 2023


Fall 2021


Fall 2020



Course Staff


Dr. Agha Ali Raza

Meet our machine learning experts!

Dr. Agha Ali Raza and his proficient team of teaching assistants have successfully guided nearly more than 400 students, equipping them with the essential skills and concepts, allowing them to become proficient in the field of machine learning. Alongside, the students have also been empowered to explore the latest innovations in the field independently. 

Our machine learning course is meticulously designed to offer a well-rounded educational experience. The curriculum is designed with a careful balance between the theoretical foundations and hands-on practical applications of machine learning to ensure that students can perform well regardless of whether they are working in the industry or research.     

Haris Bin Zia

M. Usama Saleem

M. Hashim Javed

Hira Dhamyal

Taimoor Arif

Dania Ahmad

Iman Ijaz

Hafizah Afirah Zahid

Adeen Amer

Shahbaz Ali

Ayesha Majid

Fatima Sohail

Sualeha Farid

Abdul Hameed

Fahad Touseef

Shumail Sajjad

Samee Arif

Ahmad Mahmood

Zohaib Khan

Saad Hassan Iqbal

Alina Faisal

Fatima Ali

Muawiz Feroze

Syed Kabir Ahmed

Rafey Rana

Zain Ali Khokar

Syeda Mah Noor

Mughees Ur Rehman

Haad Zahid

Nida Tanveer




Our special thanks to Professors Roni Rosenfeld, Kilian Weinberger, Andrew Ng, Sarmad Hussain, Dan Jurafsky, James H. Martin, Christopher Manning, and Victor Levrenko, whose Machine Learning, Natural Language Processing, and Speech Processing courses inspired the contents of several lectures in this series. 

We would also like to express our gratitude towards Kalid Azad (Better Explained), Joshua Starmer (StatQuest), and Grant Sanderson (3blue1brown), as their amazing educational videos motivated and simplified several complex explanations in this course.