Comparing various classification algorithms to predict whether an adult earns more than $50,000 a year. Emphasis is on feature engineering, data preprocessing with One-Hot Encoding, and model optimization through hyperparameter tuning.

Comparing various regression algorithms to predict house prices in relation to the distance from the coast. Emphasis is on feature engineering, data preprocessing with One-Hot Encoding, and model optimization through hyperparameter tuning.

Practicing various algorithms and techniques. This includes supervised, unsupervised, and reinforcement learning algorithms, as well as feature engineering, data preprocessing, and hyperparameter tuning.

Practicing various Reinforcement Learning algorithms and techniques. This includes Q-Learning, Deep Q-Learning, and Asynchronous Advantage Actor-Critic (A3C) algorithms.

Implementing algorithms from scratch such as the Nearest Neighbours algorithm. Requires an understanding of the Mathematics behind the algorithms and the ability to implement them.

Be able to use and implement algorithms, with the Lasso and inductive conformal prediction algorithms as examples.

Be able to use and implement algorithms, with the SVM, neural networks, and cross-conformal prediction algorithms as examples.

Implemented various algorithms and techniques learnt during the course, such as Nearest Neighbours, Conformal Prediction, Regression algorithms, data preprocessing, kernels, Neural Networks, SVMs, etc.

Exploring valuation of options using methods like Black-Scholes, binomial trees, and Monte Carlo. Also includes theoretical aspects of put-call parity and financial arbitrage opportunities.

Focusing on learning generative models, using third-party models and using advanced techniques. This includes techniques such as transfer learning, LLM Agents, and Generative Models.

Pacman agent plays to win the game while handling stochasticity in the movement of agent and ghosts. Uses Markov Decision Processes, Value Iteration and other enhancements.

Segmenting images using various techniques. Specifically, used models of biological vision systems such as Simple and Complex cells, and Gabor filters.

Solutions to quizzes where various low-level and mid-level vision techniques were covered. These techniques include edge detection, image segmentation, and image filtering.