## Courses I took in Mcgill

COMP 652 Machine Learning

Linear models: linear and polynomial regression, overfitting, model selection, logistic regression, Naive Bayes

Non-linear models: decision trees, instance-based learning, boosting, neural networks

Support vector machines and kernels

Computational learning theory

Structured models: graphical models, deep belief networks

Unsupervised learning: K-means, expectation maximization, PCA and other dimensionality reduction methods

Learning in dynamical systems: Hidden Markov Models and other types of temporal/sequence models

Reinforcement learning

COMP 690 Probabilistic Analysis of Algorithms

Binary search trees, Divide-and-conquer, Randomized algorithms, Conditional branching processes, Random graphs, Combinatorial search problems, Markov chains, Random walks

MATH 556 Mathematics Statistics I

Univariate and Multivariate Distributions

Transformations and Expectations

Families of distributions: Location-Scale Families, Exponential Families, Convolution Families and Exponential Dispersion Models, Hierarchical Models

Some Inequalities: Markov’s inequality, Chebyshev’s inequality, Chernoff bounds, Cauchy-Schwarz Inequality, Jensen’s Inequality

Sampling Distributions: Sampling from a Location-Scale Family, Exponential Family, Normal Family

Convergence concepts: The Weak Law of Large Numbers, The Strong Law of Large Numbers, Weak Convergence, A Central Limit Theorem, The Delta Method

Random Number Generation

MATH 557 Mathematics Statistics II

Data Reduction and Summary: Sufficiency and Ancillarity, The Likelihood Principle

Point Estimation: Methods of Finding Estimators, Methods of Evaluating Estimators

Hypothesis Testing: Objectives, Test Construction, Test Evaluation, Interval Estimation, Methods of Finding Interval Estimators, Methods of Evaluating Interval Estimators

Asymptotic Considerations: Point Estimation, Robustness, Hypothesis Testing, Interval Estimation

Inference Paradigms: Comparing Frequentist and Bayesian Approaches

COMP 521 Modern Computer Games

Game genres, theories of fun, Storytelling and narratives, Narrative modelling and analysis, Game engines, Game physics: physical simulation, approximation techniques, Collision detection and response, Path-finding, Group behaviour, NPCs, Opponents and strategy, Scripting and testing. Multiplayer games, Networking and distributed game design, Consistency, dead-reckoning, timing, Massively multiplayer games: persistence, scalability, Cheating, Concurrency

FRSL 302: French Listening Comp & Oral Exp 1

FRSL 303: French Listening Comp & Oral Exp 2