The term structure of interest rates is relevant to economists as it reflects the information available to the market about the time value of money in the future. Affine term structure models such as short rate models have been used in interest rate modelling over the past years to determine the mechanisms driving the term structure. Machine learning approaches are explored in this thesis and compared to the traditional econometric approach, specifically the Vasicek model. Multifactor Vasicek models are considered as the one factor model is found not adequate to characterize the term structure of interest rates.  Since the short rates are not observable the Kalman filter approach is used in estimating the parameters of the Vasicek model.  This thesis utilizes the Canadian zero-coupon bond price data in the implementation of both methods and it is observed from both methods that increasing the number of factors to three increases the ability to capture the curvature of the yield curve. The first factor is identified to be responsible for the level of the yield curve, the second factor the slope and third factor the curvature of the yield curve. This is consistent with results obtained from previous work on term structure models. The results from this work indicates that the machine learning technique, specifically the first three principal components of the Principal Component Analysis (PCA), outperforms the Vasicek model in fitting the yield curve.