Topic: Introduction to Deep Learning and Review of Fundamentals
Sub-Topics: Introduction to AI, ML, and DL, Intro to Python, Review of Math (Linear Algebra and Probability), Review of Math (Calculus, Statistics, and Performance Metrics), Understanding Datasets and their Types (Structured. Unstructured, Semi-structured)
Instructor: Hashir Moheed Kiani
Industry Supervised Activities: Design and prepare for the development of a score prediction for a cricket match by understanding how the data will be gathered and how a solution will be delivered.
Topic: Machine Learning
Sub-Topics: Supervised and Unsupervised Learning, Feature Engineering, Linear Regression, Logistic Regression, KNN, SVM, Decision Trees/Random Forest, Ensemble Models
Instructor: Dr. Muhammad Naseer Bajwa
Industry Supervised Activities: Develop and evaluate a score predictor model using a dataset of the first Innings statistics of T20 international cricket matches using linear regression
Fundamentals of Deep Neural Networks
Sub-Topics: From Perceptron to Multi-Layer Perception, Feedforward Networks and Activations, Backpropagation and Optimisation, Training Neural Network, Identifying, and fixing Underfitting and Overfitting, Regularisation.
Instructor: Dr. Rabia Irfan
Industry Supervised Activities: Hands-on to configure, set up, deploy, and run algorithms on servers in a production environment.
Computer Vision
Sub-Topics: Image Classification, Transfer Learning using Pre-trained CNNs, Object Detection, Image Segmentation
Instructor: Dr. Momina Moetesum and Dr. Junaid Younas
Industry Supervised Activities: Hands-on activities to normalize the given dataset and extract insights from it using Python.
Natural Language Processing
Sub-Topics: Fundamentals of NLP (text preprocessing, word vectors, embeddings), Sequence Modelling using RNNs and LSTM, Machine Translation using seq-to-seq models and Cross-Attention, Self-Attention, and Transformers
Instructor: Dr. Seemab Latif
Industry Supervised Activities: Time series Forecasting of sensor data using RNN, LSTM or GRU.
Special Topics in Deep Learning
Sub-Topics: Bias Identification and Mitigation, Explainable AI (GradCAM or any other method), Diffusion Models, Generative Adversarial Networks
Instructor: Dr. Junaid Younas
Instructor: Dr. Muhammad Sadiq Amin
Project Planning and Refinemen
Sub-Topics: Ethical and Responsible AI, Failure First Approach, AutoML: Quick and Non-dirty Approach, Unreasonable Effectiveness of Data
Instructor: Dr. Wajahat Hussain