In this course, students will learn Introduction to Reinforcement Learning and its applications, Multi-armed bandits, Markov Decision Processes (MDP), Value Iteration, Policy Iteration, Monte Carlo algorithms, Temporal Difference (TD) evaluation, Q-Learning, SARSA, ESARSA, Introduction to DRL, Policy Gradient, Actor-Critic, State-of-the-art Algorithms (PPO).

In this course, students will learn Introduction to mobile robots as the key part of SLAM, Introduction to Mapping and Localization, Well-known and State-of-the-art approaches for localization and mapping, Recursive Bayesian estimation, Kalman Filter, Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Histogram Filter Localization Particle filter, Occupancy Grid Mapping, Graph based approaches, Iterative Closest Point (ICP), Hierarchical approaches, Odometry Based approaches, Optimization-based approaches, ORB-SLAM.

Machine learning is one of the interesting fields that is based on learning-based algorithms and it is a part of artificial intelligence. This course introduces the basic principles of machine learning in which prediction and modeling play important roles. Topics in The course: Statistical Learning - Linear regression - Classification and Clustering algorithms - Support Vector Machine (SVM) - Neural Networks and Convolutional Neural Networks (CNNs) - Recurrent Neural Networks (RNNs) - Deep Belief Networks (DBNs) - Transfer Learning - Ensemble learning techniques - Multi-task Learning (MLT) - Decision Tree - State-of-the-art algorithms.